==> Building on scovillain ==> Checking for remote environment... ==> Syncing package to remote host... sending incremental file list created directory packages/python-statsmodels ./ .SRCINFO 1,028 100% 0.00kB/s 0:00:00 1,028 100% 0.00kB/s 0:00:00 (xfr#1, to-chk=7/9) .nvchecker.toml 130 100% 126.95kB/s 0:00:00 130 100% 126.95kB/s 0:00:00 (xfr#2, to-chk=6/9) LICENSE 646 100% 630.86kB/s 0:00:00 646 100% 630.86kB/s 0:00:00 (xfr#3, to-chk=5/9) PKGBUILD 1,567 100% 1.49MB/s 0:00:00 1,567 100% 1.49MB/s 0:00:00 (xfr#4, to-chk=4/9) REUSE.toml 375 100% 366.21kB/s 0:00:00 375 100% 366.21kB/s 0:00:00 (xfr#5, to-chk=3/9) python-statsmodels-0.14.6-2.log 854 100% 833.98kB/s 0:00:00 854 100% 833.98kB/s 0:00:00 (xfr#6, to-chk=2/9) LICENSES/ LICENSES/0BSD.txt -> ../LICENSE sent 2,929 bytes received 194 bytes 2,082.00 bytes/sec total size is 3,953 speedup is 1.27 ==> Patching arch to riscv64... ==> Running pkgctl build --arch riscv64 on remote host... ==> WARNING: invalid architecture: riscv64 ==> Updating pacman database cache ==> Locking pacman database cache...done [?25l:: Synchronizing package databases... core downloading... extra downloading... multilib downloading... [?25h==> Building python-statsmodels -> repo: extra -> arch: riscv64 -> worker: felix-11 ==> Building python-statsmodels for [extra] (riscv64) ==> Locking clean chroot...done [?25l:: Synchronizing package databases... core downloading... extra downloading... :: Starting full system upgrade... there is nothing to do [?25h==> Building in chroot for [extra] (riscv64)... ==> Locking clean chroot [/var/lib/archbuild/extra-riscv64/root]...done ==> Synchronizing chroot copy [/var/lib/archbuild/extra-riscv64/root] -> [felix-11]...done ==> Making package: python-statsmodels 0.14.6-2 (Sun Aug 30 23:56:58 2026) ==> Retrieving sources...  -> Cloning statsmodels git repo... Cloning into bare repository '/home/felix/packages/python-statsmodels/statsmodels'... remote: Enumerating objects: 184312, done. remote: Counting objects: 0% (1/3935) remote: Counting objects: 1% (40/3935) remote: Counting objects: 2% (79/3935) remote: Counting objects: 3% (119/3935) remote: Counting objects: 4% (158/3935) remote: Counting objects: 5% (197/3935) remote: Counting objects: 6% (237/3935) remote: Counting objects: 7% (276/3935) remote: Counting objects: 8% (315/3935) remote: Counting objects: 9% (355/3935) remote: Counting objects: 10% (394/3935) remote: Counting objects: 11% (433/3935) remote: Counting objects: 12% (473/3935) remote: Counting objects: 13% (512/3935) remote: Counting objects: 14% (551/3935) remote: Counting objects: 15% (591/3935) remote: Counting objects: 16% (630/3935) remote: Counting objects: 17% (669/3935) remote: Counting objects: 18% (709/3935) remote: Counting objects: 19% (748/3935) remote: Counting objects: 20% (787/3935) remote: Counting 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remote: Counting objects: 69% (2716/3935) remote: Counting objects: 70% (2755/3935) remote: Counting objects: 71% (2794/3935) remote: Counting objects: 72% (2834/3935) remote: Counting objects: 73% (2873/3935) remote: Counting objects: 74% (2912/3935) remote: Counting objects: 75% (2952/3935) remote: Counting objects: 76% (2991/3935) remote: Counting objects: 77% (3030/3935) remote: Counting objects: 78% (3070/3935) remote: Counting objects: 79% (3109/3935) remote: Counting objects: 80% (3148/3935) remote: Counting objects: 81% (3188/3935) remote: Counting objects: 82% (3227/3935) remote: Counting objects: 83% (3267/3935) remote: Counting objects: 84% (3306/3935) remote: Counting objects: 85% (3345/3935) remote: Counting objects: 86% (3385/3935) remote: Counting objects: 87% (3424/3935) remote: Counting objects: 88% (3463/3935) remote: Counting objects: 89% (3503/3935) remote: Counting objects: 90% (3542/3935) remote: Counting objects: 91% (3581/3935) remote: Counting objects: 92% (3621/3935) remote: Counting objects: 93% (3660/3935) remote: Counting objects: 94% (3699/3935) remote: Counting objects: 95% (3739/3935) remote: Counting objects: 96% (3778/3935) remote: Counting objects: 97% (3817/3935) remote: Counting objects: 98% (3857/3935) remote: Counting objects: 99% (3896/3935) remote: Counting objects: 100% (3935/3935) remote: Counting objects: 100% (3935/3935), done. remote: Compressing objects: 0% (1/1090) remote: Compressing objects: 1% (11/1090) remote: Compressing objects: 2% (22/1090) remote: Compressing objects: 3% (33/1090) remote: Compressing objects: 4% (44/1090) remote: Compressing objects: 5% (55/1090) remote: Compressing objects: 6% (66/1090) remote: Compressing objects: 7% (77/1090) remote: Compressing objects: 8% (88/1090) remote: Compressing objects: 9% (99/1090) remote: Compressing objects: 10% (109/1090) remote: Compressing objects: 11% (120/1090) remote: Compressing objects: 12% (131/1090) remote: Compressing objects: 13% (142/1090) 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objects: 59% (644/1090) remote: Compressing objects: 60% (654/1090) remote: Compressing objects: 61% (665/1090) remote: Compressing objects: 62% (676/1090) remote: Compressing objects: 63% (687/1090) remote: Compressing objects: 64% (698/1090) remote: Compressing objects: 65% (709/1090) remote: Compressing objects: 66% (720/1090) remote: Compressing objects: 67% (731/1090) remote: Compressing objects: 68% (742/1090) remote: Compressing objects: 69% (753/1090) remote: Compressing objects: 70% (763/1090) remote: Compressing objects: 71% (774/1090) remote: Compressing objects: 72% (785/1090) remote: Compressing objects: 73% (796/1090) remote: Compressing objects: 74% (807/1090) remote: Compressing objects: 75% (818/1090) remote: Compressing objects: 76% (829/1090) remote: Compressing objects: 77% (840/1090) remote: Compressing objects: 78% (851/1090) remote: Compressing objects: 79% (862/1090) remote: Compressing objects: 80% (872/1090) remote: Compressing objects: 81% (883/1090) remote: Compressing objects: 82% (894/1090) remote: Compressing objects: 83% (905/1090) remote: Compressing objects: 84% (916/1090) remote: Compressing objects: 85% (927/1090) remote: Compressing objects: 86% (938/1090) remote: Compressing objects: 87% (949/1090) remote: Compressing objects: 88% (960/1090) remote: Compressing objects: 89% (971/1090) remote: Compressing objects: 90% (981/1090) remote: Compressing objects: 91% (992/1090) remote: Compressing objects: 92% (1003/1090) remote: Compressing objects: 93% (1014/1090) remote: Compressing objects: 94% (1025/1090) remote: Compressing objects: 95% (1036/1090) remote: Compressing objects: 96% (1047/1090) remote: Compressing objects: 97% (1058/1090) remote: Compressing objects: 98% (1069/1090) remote: Compressing objects: 99% (1080/1090) remote: Compressing objects: 100% (1090/1090) remote: Compressing objects: 100% (1090/1090), done. Receiving objects: 0% (1/184312) Receiving objects: 1% (1844/184312) Receiving objects: 1% (3144/184312), 748.01 KiB | 970.00 KiB/s Receiving objects: 2% (3687/184312), 748.01 KiB | 970.00 KiB/s Receiving objects: 3% (5530/184312), 1.40 MiB | 1.10 MiB/s Receiving objects: 4% (7373/184312), 2.07 MiB | 1.15 MiB/s Receiving objects: 4% (8305/184312), 2.07 MiB | 1.15 MiB/s Receiving objects: 5% (9216/184312), 2.07 MiB | 1.15 MiB/s Receiving objects: 6% (11059/184312), 2.07 MiB | 1.15 MiB/s Receiving objects: 7% (12902/184312), 3.54 MiB | 1.51 MiB/s Receiving objects: 8% (14745/184312), 3.54 MiB | 1.51 MiB/s Receiving objects: 9% (16589/184312), 3.54 MiB | 1.51 MiB/s Receiving objects: 9% (17405/184312), 5.31 MiB | 1.81 MiB/s Receiving objects: 9% (17443/184312), 8.86 MiB | 2.24 MiB/s Receiving objects: 10% (18432/184312), 12.75 MiB | 2.57 MiB/s Receiving objects: 10% (18462/184312), 12.75 MiB | 2.57 MiB/s Receiving objects: 10% (20002/184312), 17.18 MiB | 3.36 MiB/s Receiving objects: 11% (20275/184312), 17.18 MiB | 3.36 MiB/s Receiving objects: 12% (22118/184312), 20.12 MiB | 3.86 MiB/s Receiving objects: 12% (23499/184312), 22.65 MiB | 4.14 MiB/s Receiving objects: 13% (23961/184312), 22.65 MiB | 4.14 MiB/s Receiving objects: 14% (25804/184312), 22.65 MiB | 4.14 MiB/s Receiving objects: 15% (27647/184312), 25.18 MiB | 4.38 MiB/s Receiving objects: 16% (29490/184312), 25.18 MiB | 4.38 MiB/s Receiving objects: 16% (30804/184312), 28.01 MiB | 4.58 MiB/s Receiving objects: 17% (31334/184312), 28.01 MiB | 4.58 MiB/s Receiving objects: 18% (33177/184312), 30.11 MiB | 4.63 MiB/s Receiving objects: 19% (35020/184312), 30.11 MiB | 4.63 MiB/s Receiving objects: 20% (36863/184312), 30.11 MiB | 4.63 MiB/s Receiving objects: 20% (37048/184312), 30.11 MiB | 4.63 MiB/s Receiving objects: 21% (38706/184312), 33.32 MiB | 4.89 MiB/s Receiving objects: 22% (40549/184312), 33.32 MiB | 4.89 MiB/s Receiving objects: 23% (42392/184312), 33.32 MiB | 4.89 MiB/s Receiving objects: 24% (44235/184312), 36.45 MiB | 5.16 MiB/s Receiving objects: 25% (46078/184312), 36.45 MiB | 5.16 MiB/s Receiving objects: 26% (47922/184312), 36.45 MiB | 5.16 MiB/s Receiving objects: 26% (48615/184312), 36.45 MiB | 5.16 MiB/s Receiving objects: 27% (49765/184312), 39.11 MiB | 5.21 MiB/s Receiving objects: 28% (51608/184312), 39.11 MiB | 5.21 MiB/s Receiving objects: 29% (53451/184312), 39.11 MiB | 5.21 MiB/s Receiving objects: 30% (55294/184312), 39.11 MiB | 5.21 MiB/s Receiving objects: 31% (57137/184312), 41.98 MiB | 5.40 MiB/s Receiving objects: 32% (58980/184312), 41.98 MiB | 5.40 MiB/s Receiving objects: 33% (60823/184312), 41.98 MiB | 5.40 MiB/s Receiving objects: 33% (61676/184312), 45.12 MiB | 5.38 MiB/s Receiving objects: 34% (62667/184312), 45.12 MiB | 5.38 MiB/s Receiving objects: 35% (64510/184312), 45.12 MiB | 5.38 MiB/s Receiving objects: 36% (66353/184312), 45.12 MiB | 5.38 MiB/s Receiving objects: 37% (68196/184312), 45.12 MiB | 5.38 MiB/s Receiving objects: 38% (70039/184312), 48.39 MiB | 5.51 MiB/s Receiving objects: 39% (71882/184312), 48.39 MiB | 5.51 MiB/s Receiving objects: 40% (73725/184312), 48.39 MiB | 5.51 MiB/s Receiving objects: 41% (75568/184312), 48.39 MiB | 5.51 MiB/s Receiving objects: 42% (77412/184312), 48.39 MiB | 5.51 MiB/s Receiving objects: 43% (79255/184312), 48.39 MiB | 5.51 MiB/s Receiving objects: 44% (81098/184312), 48.39 MiB | 5.51 MiB/s Receiving objects: 44% (82592/184312), 48.39 MiB | 5.51 MiB/s Receiving objects: 45% (82941/184312), 48.39 MiB | 5.51 MiB/s Receiving objects: 46% (84784/184312), 48.39 MiB | 5.51 MiB/s Receiving objects: 47% (86627/184312), 51.76 MiB | 5.66 MiB/s Receiving objects: 48% (88470/184312), 51.76 MiB | 5.66 MiB/s Receiving objects: 49% (90313/184312), 51.76 MiB | 5.66 MiB/s Receiving objects: 50% (92156/184312), 51.76 MiB | 5.66 MiB/s Receiving objects: 51% (94000/184312), 51.76 MiB | 5.66 MiB/s Receiving objects: 52% (95843/184312), 51.76 MiB | 5.66 MiB/s Receiving objects: 53% (97686/184312), 51.76 MiB | 5.66 MiB/s Receiving objects: 54% (99529/184312), 51.76 MiB | 5.66 MiB/s Receiving objects: 55% (101372/184312), 51.76 MiB | 5.66 MiB/s Receiving objects: 55% (102879/184312), 55.14 MiB | 5.37 MiB/s Receiving objects: 56% (103215/184312), 55.14 MiB | 5.37 MiB/s Receiving objects: 57% (105058/184312), 55.14 MiB | 5.37 MiB/s Receiving objects: 58% (106901/184312), 55.14 MiB | 5.37 MiB/s Receiving objects: 59% (108745/184312), 55.14 MiB | 5.37 MiB/s Receiving objects: 60% (110588/184312), 55.14 MiB | 5.37 MiB/s Receiving objects: 61% (112431/184312), 57.02 MiB | 5.41 MiB/s Receiving objects: 62% (114274/184312), 57.02 MiB | 5.41 MiB/s Receiving objects: 63% (116117/184312), 57.02 MiB | 5.41 MiB/s Receiving objects: 64% (117960/184312), 57.02 MiB | 5.41 MiB/s Receiving objects: 65% (119803/184312), 57.02 MiB | 5.41 MiB/s Receiving objects: 65% (120496/184312), 57.02 MiB | 5.41 MiB/s Receiving objects: 66% (121646/184312), 57.02 MiB | 5.41 MiB/s Receiving objects: 67% (123490/184312), 59.13 MiB | 5.19 MiB/s Receiving objects: 68% (125333/184312), 59.13 MiB | 5.19 MiB/s Receiving objects: 69% (127176/184312), 59.13 MiB | 5.19 MiB/s Receiving objects: 70% (129019/184312), 59.13 MiB | 5.19 MiB/s Receiving objects: 71% (130862/184312), 59.13 MiB | 5.19 MiB/s Receiving objects: 72% (132705/184312), 59.13 MiB | 5.19 MiB/s Receiving objects: 73% (134548/184312), 62.13 MiB | 5.17 MiB/s Receiving objects: 74% (136391/184312), 62.13 MiB | 5.17 MiB/s Receiving objects: 75% (138234/184312), 62.13 MiB | 5.17 MiB/s Receiving objects: 75% (138530/184312), 62.13 MiB | 5.17 MiB/s Receiving objects: 76% (140078/184312), 67.39 MiB | 5.11 MiB/s Receiving objects: 76% (141064/184312), 70.17 MiB | 5.09 MiB/s Receiving objects: 77% (141921/184312), 70.17 MiB | 5.09 MiB/s Receiving objects: 78% (143764/184312), 73.00 MiB | 5.03 MiB/s Receiving objects: 79% (145607/184312), 73.00 MiB | 5.03 MiB/s Receiving objects: 80% (147450/184312), 73.00 MiB | 5.03 MiB/s Receiving objects: 81% (149293/184312), 73.00 MiB | 5.03 MiB/s Receiving objects: 82% (151136/184312), 73.00 MiB | 5.03 MiB/s Receiving objects: 82% (151213/184312), 75.87 MiB | 4.96 MiB/s Receiving objects: 83% (152979/184312), 75.87 MiB | 4.96 MiB/s Receiving objects: 84% (154823/184312), 75.87 MiB | 4.96 MiB/s Receiving objects: 85% (156666/184312), 75.87 MiB | 4.96 MiB/s Receiving objects: 86% (158509/184312), 75.87 MiB | 4.96 MiB/s Receiving objects: 87% (160352/184312), 78.79 MiB | 5.19 MiB/s Receiving objects: 88% (162195/184312), 78.79 MiB | 5.19 MiB/s Receiving objects: 89% (164038/184312), 78.79 MiB | 5.19 MiB/s Receiving objects: 90% (165881/184312), 78.79 MiB | 5.19 MiB/s Receiving objects: 90% (166258/184312), 81.75 MiB | 5.40 MiB/s Receiving objects: 91% (167724/184312), 81.75 MiB | 5.40 MiB/s Receiving objects: 92% (169568/184312), 81.75 MiB | 5.40 MiB/s Receiving objects: 93% (171411/184312), 81.75 MiB | 5.40 MiB/s Receiving objects: 94% (173254/184312), 81.75 MiB | 5.40 MiB/s Receiving objects: 95% (175097/184312), 81.75 MiB | 5.40 MiB/s Receiving objects: 95% (176301/184312), 84.31 MiB | 5.49 MiB/s Receiving objects: 96% (176940/184312), 86.18 MiB | 5.22 MiB/s Receiving objects: 97% (178783/184312), 88.94 MiB | 5.28 MiB/s Receiving objects: 98% (180626/184312), 88.94 MiB | 5.28 MiB/s Receiving objects: 99% (182469/184312), 88.94 MiB | 5.28 MiB/s Receiving objects: 99% (183779/184312), 88.94 MiB | 5.28 MiB/s remote: Total 184312 (delta 3261), reused 2846 (delta 2843), pack-reused 180377 (from 3) Receiving objects: 100% (184312/184312), 88.94 MiB | 5.28 MiB/s Receiving objects: 100% (184312/184312), 91.28 MiB | 4.55 MiB/s, done. 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Passed ==> Making package: python-statsmodels 0.14.6-2 (Sun Aug 30 23:58:18 2026) ==> Checking runtime dependencies... ==> Installing missing dependencies... [?25lresolving dependencies... looking for conflicting packages... Package (18) New Version Net Change Download Size extra/blas 3.12.1-2 0.43 MiB extra/cblas 3.12.1-2 0.31 MiB extra/lapack 3.12.1-2 9.09 MiB extra/python-certifi 2026.07.22-1 0.02 MiB extra/python-charset-normalizer 3.4.7-1 0.95 MiB extra/python-dateutil 2.9.0-8.1 1.03 MiB extra/python-idna 3.19-1 0.66 MiB extra/python-packaging 26.3-1 1.58 MiB extra/python-platformdirs 4.11.5-1 0.47 MiB extra/python-pooch 1.9.0-1 0.75 MiB extra/python-pytz 2026.1-1 0.17 MiB extra/python-requests 2.34.2-1 0.76 MiB extra/python-six 1.17.0-3 0.12 MiB extra/python-urllib3 2.6.3-1 1.44 MiB extra/python-numpy 2.5.2-1 42.43 MiB extra/python-pandas 2.3.3-2 107.45 MiB extra/python-patsy 1.0.2-2 2.23 MiB 0.36 MiB extra/python-scipy 1.18.1-1 112.78 MiB Total Download Size: 0.36 MiB Total Installed Size: 282.67 MiB :: Proceed with installation? [Y/n] :: Retrieving packages... python-patsy-1.0.2-2-any downloading... checking keyring... checking package integrity... loading package files... checking for file conflicts... :: Processing package changes... installing blas... installing cblas... installing lapack... installing python-numpy... Optional dependencies for python-numpy blas-openblas: faster linear algebra installing python-platformdirs... installing python-packaging... installing python-charset-normalizer... installing python-idna... installing python-urllib3... Optional dependencies for python-urllib3 python-brotli: Brotli support python-brotlicffi: Brotli support python-h2: HTTP/2 support python-pysocks: SOCKS support installing python-certifi... installing python-requests... Optional dependencies for python-requests python-chardet: alternative character encoding library python-pysocks: SOCKS proxy support installing python-pooch... Optional dependencies for python-pooch python-paramiko: for SFTP downloads python-tqdm: for printing a download progress bar installing python-scipy... Optional dependencies for python-scipy python-pillow: for image saving module installing python-six... installing python-dateutil... installing python-pytz... installing python-pandas... Optional dependencies for python-pandas python-pandas-datareader: pandas.io.data replacement (recommended) python-numexpr: accelerating certain numerical operations (recommended) python-bottleneck: accelerating certain types of nan evaluations (recommended) python-matplotlib: plotting python-jinja: conditional formatting with DataFrame.style python-tabulate: printing in Markdown-friendly format python-scipy: miscellaneous statistical functions [installed] python-numba: alternative execution engine python-xarray: pandas-like API for N-dimensional data python-xlrd: Excel XLS input python-xlwt: Excel XLS output python-openpyxl: Excel XLSX input/output python-xlsxwriter: alternative Excel XLSX output python-beautifulsoup4: read_html function (in any case) python-html5lib: read_html function (and/or python-lxml) python-lxml: read_xml, to_xml and read_html function (and/or python-html5lib) python-sqlalchemy: SQL database support python-psycopg2: PostgreSQL engine for sqlalchemy python-pymysql: MySQL engine for sqlalchemy python-pytables: HDF5-based reading / writing python-blosc: for msgpack compression using blosc zlib: compression for msgpack [installed] python-pyarrow: Parquet, ORC and feather reading/writing python-fsspec: handling files aside from local and HTTP python-qtpy: read_clipboard function (only one needed) xclip: read_clipboard function (only one needed) xsel: read_clipboard function (only one needed) python-brotli: Brotli compression python-snappy: Snappy compression python-zstandard: Zstandard (zstd) compression installing python-patsy... Optional dependencies for python-patsy python-scipy: needed for spline-related functions [installed] :: Running post-transaction hooks... (1/1) Arming ConditionNeedsUpdate... [?25h==> Checking buildtime dependencies... ==> Installing missing dependencies... [?25lresolving dependencies... looking for conflicting packages... Package (61) New Version Net Change Download Size extra/aom 3.15.0-1 4.98 MiB extra/dav1d 1.5.4-1 0.71 MiB extra/freetype2 2.14.3-1 1.61 MiB extra/fribidi 1.0.16-2 0.24 MiB extra/graphite 1:1.3.15-1 0.18 MiB extra/harfbuzz 14.4.0-1 4.70 MiB extra/jbigkit 2.1-8 0.13 MiB extra/lcms2 2.19.1-1 0.66 MiB extra/libavif 1.4.2-1 0.77 MiB extra/libdeflate 1.26-1 0.10 MiB extra/libimagequant 4.4.1-2 0.55 MiB extra/libjpeg-turbo 3.2.0-2 2.24 MiB extra/libpng 1.6.58-2 0.54 MiB extra/libraqm 0.11.0-1 0.19 MiB extra/libtiff 4.7.2-1 1.18 MiB extra/libwebp 1.6.0-2 0.64 MiB extra/libxau 1.0.12-1 0.02 MiB extra/libxcb 1.17.0-1 3.69 MiB extra/libxdmcp 1.1.5-2 0.13 MiB extra/libyuv r2921+644251f25-1 1.16 MiB extra/openjpeg2 2.5.4-1 13.30 MiB extra/perl-error 0.17030-3 0.04 MiB extra/perl-mailtools 2.22-3 0.10 MiB extra/perl-timedate 2.35-1 0.15 MiB extra/python-autocommand 2.2.2-9 0.08 MiB extra/python-contourpy 1.3.3-4 0.91 MiB extra/python-cycler 0.12.1-4 0.07 MiB extra/python-execnet 2.1.2-3 0.55 MiB extra/python-fonttools 4.63.0-1 20.97 MiB extra/python-iniconfig 2.3.0-1 0.07 MiB extra/python-jaraco.collections 5.1.0-3 0.11 MiB extra/python-jaraco.context 6.1.2-1 0.06 MiB extra/python-jaraco.functools 4.1.0-3 0.07 MiB extra/python-jaraco.text 4.0.0-4 0.08 MiB extra/python-kiwisolver 1.5.1-1 0.14 MiB extra/python-more-itertools 11.1.0-1 0.77 MiB extra/python-pillow 12.3.0-1 4.85 MiB extra/python-pkg_resources 81.0.0-1 0.50 MiB extra/python-pluggy 1.6.0-3.1 0.23 MiB extra/python-pygments 2.20.0-1 15.36 MiB extra/python-pyparsing 3.3.2-1.1 1.55 MiB extra/python-pyproject-hooks 1.2.0-6 0.11 MiB extra/python-setuptools 1:82.0.1-1 7.35 MiB extra/python-typing_extensions 4.16.0-1 0.53 MiB extra/python-vcs-versioning 2.3.1-1 1.18 MiB extra/qhull 2020.2-5 12.82 MiB extra/rav1e 0.8.1-3 4.85 MiB extra/svt-av1 4.2.0-1 2.16 MiB extra/xcb-proto 1.17.0-4 1.03 MiB extra/xorgproto 2025.1-1 1.47 MiB extra/zlib-ng 2.3.3-1 0.23 MiB extra/cython 3.2.9-1 18.37 MiB extra/git 2.55.0-1 29.92 MiB extra/python-build 1.4.3-1 0.26 MiB extra/python-installer 1.0.0-1 0.20 MiB extra/python-joblib 1.5.3-1 2.84 MiB 0.53 MiB extra/python-matplotlib 3.11.1-1 32.21 MiB extra/python-pytest 1:9.0.3-1 4.86 MiB extra/python-pytest-xdist 3.8.0-3 0.56 MiB extra/python-setuptools-scm 10.2.1-1 0.23 MiB extra/python-wheel 0.48.0-1 0.34 MiB Total Download Size: 0.53 MiB Total Installed Size: 205.91 MiB :: Proceed with installation? [Y/n] :: Retrieving packages... python-joblib-1.5.3-1-any downloading... checking keyring... checking package integrity... loading package files... checking for file conflicts... :: Processing package changes... installing python-more-itertools... installing python-jaraco.functools... installing python-jaraco.context... installing python-autocommand... installing python-jaraco.text... Optional dependencies for python-jaraco.text python-inflect: for show-newlines script installing python-jaraco.collections... installing python-wheel... Optional dependencies for python-wheel python-keyring: for wheel.signatures python-xdg: for wheel.signatures python-setuptools: for legacy bdist_wheel subcommand [pending] installing python-typing_extensions... installing python-pkg_resources... installing python-setuptools... installing python-vcs-versioning... Optional dependencies for python-vcs-versioning python-rich: formatting of log messages installing python-setuptools-scm... Optional dependencies for python-setuptools-scm python-rich: use rich as console log handler installing python-pyproject-hooks... installing python-build... Optional dependencies for python-build python-pip: to use as the Python package installer (default) python-uv: to use as the Python package installer python-virtualenv: to use virtualenv for build isolation installing python-installer... installing python-pygments... installing cython... installing perl-error... installing perl-timedate... installing perl-mailtools... installing zlib-ng... installing git... Optional dependencies for git git-zsh-completion: upstream zsh completion tk: gitk and git gui openssh: ssh transport and crypto man: show help with `git command --help` perl-libwww: git svn perl-term-readkey: git svn and interactive.singlekey setting perl-io-socket-ssl: git send-email TLS support perl-authen-sasl: git send-email TLS support perl-cgi: gitweb (web interface) support python: git svn & git p4 [installed] subversion: git svn org.freedesktop.secrets: keyring credential helper libsecret: libsecret credential helper [installed] less: the default pager for git installing python-iniconfig... installing python-pluggy... installing python-pytest... installing python-execnet... installing python-pytest-xdist... Optional dependencies for python-pytest-xdist python-psutil: to use psutil for detection of the number of CPUs available python-setproctitle: to update the process title installing python-joblib... Optional dependencies for python-joblib python-distributed: for dask parallel backend python-lz4: for compressed serialization python-numpy: for array manipulation [installed] python-psutil: to mitigate memory leaks in worker processes installing libpng... installing freetype2... Optional dependencies for freetype2 harfbuzz: Improved autohinting [pending] installing python-contourpy... Optional dependencies for python-contourpy python-matplotlib: matplotlib renderer [pending] installing python-cycler... installing python-fonttools... Optional dependencies for python-fonttools python-brotli: to compress/decompress WOFF 2.0 web fonts python-fs: to read/write UFO source files python-lxml: faster backend for XML files reading/writing python-lz4: for graphite type tables in ttLib/tables python-matplotlib: for visualizing DesignSpaceDocument and resulting VariationModel [pending] python-pyqt5: for drawing glyphs with Qt’s QPainterPath python-reportlab: to drawing glyphs as PNG images python-scipy: for finding wrong contour/component order between different masters [installed] python-sympy: for symbolic font statistics analysis python-uharfbuzz: to use the Harfbuzz Repacker for packing GSUB/GPOS tables python-unicodedata2: for displaying the Unicode character names when dumping the cmap table with ttx python-zopfli: faster backend fom WOFF 1.0 web fonts compression installing python-kiwisolver... installing libjpeg-turbo... installing jbigkit... installing libdeflate... installing libwebp... Optional dependencies for libwebp libwebp-utils: WebP conversion and inspection tools installing libtiff... Optional dependencies for libtiff freeglut: for using tiffgt installing lcms2... installing aom... installing dav1d... Optional dependencies for dav1d dav1d-doc: HTML documentation installing libyuv... installing rav1e... installing svt-av1... installing libavif... installing fribidi... installing graphite... Optional dependencies for graphite graphite-docs: Documentation installing harfbuzz... Optional dependencies for harfbuzz harfbuzz-utils: utilities installing libraqm... installing openjpeg2... installing libimagequant... installing xcb-proto... installing xorgproto... installing libxdmcp... installing libxau... installing libxcb... installing python-pillow... Optional dependencies for python-pillow libwebp: for webp images [installed] tk: for the ImageTK module python-olefile: OLE2 file support python-pyqt6: for the ImageQt module python-defusedxml: for reading XMP tags installing python-pyparsing... Optional dependencies for python-pyparsing python-railroad-diagrams: for generating Railroad Diagrams python-jinja: for generating Railroad Diagrams installing qhull... installing python-matplotlib... Optional dependencies for python-matplotlib tk: Tk{Agg,Cairo} backends pyside6: alternative for Qt6{Agg,Cairo} backends python-pyqt6: Qt6{Agg,Cairo} backends python-gobject: for GTK{3,4}{Agg,Cairo} backend python-wxpython: WX{Agg,Cairo} backend python-cairo: {GTK{3,4},Qt{5,6},Tk,WX}Cairo backends python-cairocffi: alternative for Cairo backends python-tornado: WebAgg backend ffmpeg: for saving movies imagemagick: for saving animated gifs ghostscript: usetex dependencies texlive-binextra: usetex dependencies texlive-fontsrecommended: usetex dependencies texlive-latexrecommended: usetex usage with pdflatex python-certifi: https support [installed] :: Running post-transaction hooks... (1/4) Creating system user accounts... Creating group 'git' with GID 968. Creating user 'git' (git daemon user) with UID 968 and GID 968. (2/4) Reloading system manager configuration... Skipped: Current root is not booted. (3/4) Arming ConditionNeedsUpdate... (4/4) Checking for old perl modules... [?25h==> Retrieving sources... ==> WARNING: Skipping all source file integrity checks. ==> Extracting sources...  -> Creating working copy of statsmodels git repo... Cloning into 'statsmodels'... done. 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Compiling statsmodels/tsa/exponential_smoothing/_ets_smooth.pyx because it changed. Compiling statsmodels/tsa/_innovations.pyx because it changed. Compiling statsmodels/tsa/regime_switching/_hamilton_filter.pyx because it changed. Compiling statsmodels/tsa/regime_switching/_kim_smoother.pyx because it changed. Compiling statsmodels/tsa/innovations/_arma_innovations.pyx because it changed. Compiling statsmodels/nonparametric/linbin.pyx because it changed. Compiling statsmodels/robust/_qn.pyx because it changed. Compiling statsmodels/nonparametric/_smoothers_lowess.pyx because it changed. Compiling statsmodels/tsa/statespace/_initialization.pyx because it changed. Compiling statsmodels/tsa/statespace/_representation.pyx because it changed. Compiling statsmodels/tsa/statespace/_kalman_filter.pyx because it changed. Compiling statsmodels/tsa/statespace/_filters/_conventional.pyx because it changed. Compiling statsmodels/tsa/statespace/_filters/_inversions.pyx because it changed. Compiling statsmodels/tsa/statespace/_filters/_univariate.pyx because it changed. Compiling statsmodels/tsa/statespace/_filters/_univariate_diffuse.pyx because it changed. Compiling statsmodels/tsa/statespace/_kalman_smoother.pyx because it changed. Compiling statsmodels/tsa/statespace/_smoothers/_alternative.pyx because it changed. Compiling statsmodels/tsa/statespace/_smoothers/_classical.pyx because it changed. Compiling statsmodels/tsa/statespace/_smoothers/_conventional.pyx because it changed. Compiling statsmodels/tsa/statespace/_smoothers/_univariate.pyx because it changed. Compiling statsmodels/tsa/statespace/_smoothers/_univariate_diffuse.pyx because it changed. Compiling statsmodels/tsa/statespace/_simulation_smoother.pyx because it changed. Compiling statsmodels/tsa/statespace/_cfa_simulation_smoother.pyx because it changed. Compiling statsmodels/tsa/statespace/_tools.pyx because it changed. [ 1/26] Cythonizing statsmodels/nonparametric/_smoothers_lowess.pyx [ 2/26] Cythonizing statsmodels/nonparametric/linbin.pyx [ 3/26] Cythonizing statsmodels/robust/_qn.pyx [ 4/26] Cythonizing statsmodels/tsa/_innovations.pyx [ 5/26] Cythonizing statsmodels/tsa/exponential_smoothing/_ets_smooth.pyx [ 6/26] Cythonizing statsmodels/tsa/holtwinters/_exponential_smoothers.pyx [ 7/26] Cythonizing statsmodels/tsa/innovations/_arma_innovations.pyx [ 8/26] Cythonizing statsmodels/tsa/regime_switching/_hamilton_filter.pyx [ 9/26] Cythonizing statsmodels/tsa/regime_switching/_kim_smoother.pyx [10/26] Cythonizing statsmodels/tsa/statespace/_cfa_simulation_smoother.pyx [11/26] Cythonizing statsmodels/tsa/statespace/_filters/_conventional.pyx [12/26] Cythonizing statsmodels/tsa/statespace/_filters/_inversions.pyx [13/26] Cythonizing statsmodels/tsa/statespace/_filters/_univariate.pyx [14/26] Cythonizing statsmodels/tsa/statespace/_filters/_univariate_diffuse.pyx [15/26] Cythonizing statsmodels/tsa/statespace/_initialization.pyx [16/26] Cythonizing statsmodels/tsa/statespace/_kalman_filter.pyx [17/26] Cythonizing statsmodels/tsa/statespace/_kalman_smoother.pyx [18/26] Cythonizing statsmodels/tsa/statespace/_representation.pyx [19/26] Cythonizing statsmodels/tsa/statespace/_simulation_smoother.pyx [20/26] Cythonizing statsmodels/tsa/statespace/_smoothers/_alternative.pyx [21/26] Cythonizing statsmodels/tsa/statespace/_smoothers/_classical.pyx [22/26] Cythonizing statsmodels/tsa/statespace/_smoothers/_conventional.pyx [23/26] Cythonizing statsmodels/tsa/statespace/_smoothers/_univariate.pyx [24/26] Cythonizing statsmodels/tsa/statespace/_smoothers/_univariate_diffuse.pyx [25/26] Cythonizing statsmodels/tsa/statespace/_tools.pyx [26/26] Cythonizing statsmodels/tsa/stl/_stl.pyx /usr/lib/python3.14/site-packages/setuptools/dist.py:765: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: BSD License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! self._finalize_license_expression() running egg_info creating statsmodels.egg-info writing statsmodels.egg-info/PKG-INFO writing dependency_links to statsmodels.egg-info/dependency_links.txt writing requirements to statsmodels.egg-info/requires.txt writing top-level names to statsmodels.egg-info/top_level.txt writing manifest file 'statsmodels.egg-info/SOURCES.txt' dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative reading manifest template 'MANIFEST.in' warning: no files found matching '*.pxi' anywhere in distribution warning: no previously-included files matching '*' found under directory 'build' warning: no previously-included files matching '*' found under directory 'dist' warning: no previously-included files found matching 'docs/source/generated/*' warning: no files found matching '*' under directory 'docs/sphinxext' warning: no files found matching '*' under directory 'docs/themes' warning: no previously-included files matching '*' found under directory 'docs/build' warning: no previously-included files matching '*' found under directory 'docs/build/htmlhelp' warning: no files found matching 'statsmodels/statsmodelsdoc.chm' no previously-included directories found matching '*/__pycache__' warning: no previously-included files matching '*~' found anywhere in distribution warning: no previously-included files matching '*.swp' found anywhere in distribution warning: no previously-included files matching '*.pyc' found anywhere in distribution warning: no previously-included files matching '*.pyo' found anywhere in distribution warning: no previously-included files matching '*.bak' found anywhere in distribution adding license file 'LICENSE.txt' writing manifest file 'statsmodels.egg-info/SOURCES.txt' * Building wheel... /usr/lib/python3.14/site-packages/setuptools/dist.py:765: SetuptoolsDeprecationWarning: License classifiers are deprecated. !! ******************************************************************************** Please consider removing the following classifiers in favor of a SPDX license expression: License :: OSI Approved :: BSD License See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. ******************************************************************************** !! self._finalize_license_expression() running bdist_wheel running build running build_py creating build/lib.linux-riscv64-cpython-314/statsmodels copying statsmodels/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels copying statsmodels/_version.py -> build/lib.linux-riscv64-cpython-314/statsmodels copying statsmodels/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels copying statsmodels/conftest.py -> build/lib.linux-riscv64-cpython-314/statsmodels creating build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric copying statsmodels/nonparametric/kernels.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric copying statsmodels/nonparametric/kernels_asymmetric.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric copying statsmodels/nonparametric/kernel_density.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric copying statsmodels/nonparametric/smoothers_lowess.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric copying statsmodels/nonparametric/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric copying statsmodels/nonparametric/kde.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric copying statsmodels/nonparametric/kdetools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric copying statsmodels/nonparametric/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric copying statsmodels/nonparametric/bandwidths.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric copying statsmodels/nonparametric/_kernel_base.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric copying statsmodels/nonparametric/smoothers_lowess_old.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric copying statsmodels/nonparametric/kernel_regression.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric creating build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/correlation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/functional.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/factorplots.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/tsaplots.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/dotplots.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/gofplots.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/plottools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/tukeyplot.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/boxplots.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/_regressionplots_doc.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/utils.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/mosaicplot.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/agreement.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/regressionplots.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics copying statsmodels/graphics/plot_grids.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics creating build/lib.linux-riscv64-cpython-314/statsmodels/imputation copying statsmodels/imputation/mice.py -> build/lib.linux-riscv64-cpython-314/statsmodels/imputation copying statsmodels/imputation/ros.py -> build/lib.linux-riscv64-cpython-314/statsmodels/imputation copying statsmodels/imputation/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/imputation copying statsmodels/imputation/bayes_mi.py -> build/lib.linux-riscv64-cpython-314/statsmodels/imputation creating build/lib.linux-riscv64-cpython-314/statsmodels/tests copying statsmodels/tests/test_package.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tests copying statsmodels/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tests copying statsmodels/tests/test_x13.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels copying statsmodels/miscmodels/try_mlecov.py -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels copying statsmodels/miscmodels/count.py -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels copying statsmodels/miscmodels/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels copying statsmodels/miscmodels/nonlinls.py -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels copying statsmodels/miscmodels/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels copying statsmodels/miscmodels/ordinal_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels copying statsmodels/miscmodels/tmodel.py -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets copying statsmodels/datasets/template_data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets copying statsmodels/datasets/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets copying statsmodels/datasets/utils.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets creating build/lib.linux-riscv64-cpython-314/statsmodels/genmod copying statsmodels/genmod/bayes_mixed_glm.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod copying statsmodels/genmod/generalized_linear_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod copying statsmodels/genmod/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod copying statsmodels/genmod/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod copying statsmodels/genmod/_tweedie_compound_poisson.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod copying statsmodels/genmod/cov_struct.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod copying statsmodels/genmod/generalized_estimating_equations.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod copying statsmodels/genmod/qif.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod creating build/lib.linux-riscv64-cpython-314/statsmodels/treatment copying statsmodels/treatment/treatment_effects.py -> build/lib.linux-riscv64-cpython-314/statsmodels/treatment copying statsmodels/treatment/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/treatment creating build/lib.linux-riscv64-cpython-314/statsmodels/othermod copying statsmodels/othermod/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/othermod copying statsmodels/othermod/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/othermod copying statsmodels/othermod/betareg.py -> build/lib.linux-riscv64-cpython-314/statsmodels/othermod creating build/lib.linux-riscv64-cpython-314/statsmodels/regression copying statsmodels/regression/recursive_ls.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression copying statsmodels/regression/_tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression copying statsmodels/regression/rolling.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression copying statsmodels/regression/linear_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression copying statsmodels/regression/dimred.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression copying statsmodels/regression/mixed_linear_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression copying statsmodels/regression/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression copying statsmodels/regression/_prediction.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression copying statsmodels/regression/process_regression.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression copying statsmodels/regression/feasible_gls.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression copying statsmodels/regression/quantile_regression.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression creating build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/dist_dependence_measures.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/_inference_tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/diagnostic_gen.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/tabledist.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/anova.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/robust_compare.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/sandwich_covariance.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/multicomp.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/regularized_covariance.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/outliers_influence.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/correlation_tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/_lilliefors.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/_lilliefors_critical_values.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/diagnostic.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/gof.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/_diagnostic_other.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/inter_rater.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/proportion.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/power.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/_delta_method.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/oaxaca.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/meta_analysis.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/rates.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/contingency_tables.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/knockoff_regeffects.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/base.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/descriptivestats.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/nonparametric.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/effect_size.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/multivariate.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/weightstats.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/oneway.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/mediation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/moment_helpers.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/_knockoff.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/multitest.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/multivariate_tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/_adnorm.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/stattools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats copying statsmodels/stats/contrast.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats creating build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/l1_slsqp.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/covtype.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/elastic_net.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/transform.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/_penalized.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/l1_solvers_common.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/_parameter_inference.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/_penalties.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/_screening.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/_prediction_inference.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/_constraints.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/optimizer.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/distributed_estimation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/l1_cvxopt.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base copying statsmodels/base/wrapper.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base creating build/lib.linux-riscv64-cpython-314/statsmodels/multivariate copying statsmodels/multivariate/manova.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate copying statsmodels/multivariate/cancorr.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate copying statsmodels/multivariate/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate copying statsmodels/multivariate/plots.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate copying statsmodels/multivariate/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate copying statsmodels/multivariate/pca.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate copying statsmodels/multivariate/multivariate_ols.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate copying statsmodels/multivariate/factor.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate creating build/lib.linux-riscv64-cpython-314/statsmodels/discrete copying statsmodels/discrete/count_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete copying statsmodels/discrete/diagnostic.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete copying statsmodels/discrete/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete copying statsmodels/discrete/conditional_models.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete copying statsmodels/discrete/discrete_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete copying statsmodels/discrete/discrete_margins.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete copying statsmodels/discrete/truncated_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete copying statsmodels/discrete/_diagnostics_count.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete creating build/lib.linux-riscv64-cpython-314/statsmodels/src copying statsmodels/src/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/src creating build/lib.linux-riscv64-cpython-314/statsmodels/iolib copying statsmodels/iolib/smpickle.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib copying statsmodels/iolib/summary2.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib copying statsmodels/iolib/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib copying statsmodels/iolib/foreign.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib copying statsmodels/iolib/openfile.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib copying statsmodels/iolib/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib copying statsmodels/iolib/tableformatting.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib copying statsmodels/iolib/summary.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib copying statsmodels/iolib/table.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib copying statsmodels/iolib/stata_summary_examples.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib creating build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/decorators.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/eval_measures.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/docstring.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/sm_exceptions.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/linalg.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/sequences.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/web.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/print_version.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/grouputils.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/numdiff.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/typing.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/testing.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/catadd.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/_test_runner.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/_testing.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/parallel.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/rng_qrng.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/transform_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools copying statsmodels/tools/rootfinding.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools creating build/lib.linux-riscv64-cpython-314/statsmodels/emplike copying statsmodels/emplike/descriptive.py -> build/lib.linux-riscv64-cpython-314/statsmodels/emplike copying statsmodels/emplike/aft_el.py -> build/lib.linux-riscv64-cpython-314/statsmodels/emplike copying statsmodels/emplike/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/emplike copying statsmodels/emplike/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/emplike copying statsmodels/emplike/originregress.py -> build/lib.linux-riscv64-cpython-314/statsmodels/emplike copying statsmodels/emplike/elanova.py -> build/lib.linux-riscv64-cpython-314/statsmodels/emplike copying statsmodels/emplike/elregress.py -> build/lib.linux-riscv64-cpython-314/statsmodels/emplike creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/mlemodel.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/seasonal.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/tsatools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/arima_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/arma_mle.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/coint_tables.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/ar_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/adfvalues.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/arima_process.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/descriptivestats.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/deterministic.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/varma_process.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/_bds.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/x13.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa copying statsmodels/tsa/stattools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa creating build/lib.linux-riscv64-cpython-314/statsmodels/distributions copying statsmodels/distributions/bernstein.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions copying statsmodels/distributions/discrete.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions copying statsmodels/distributions/empirical_distribution.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions copying statsmodels/distributions/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions copying statsmodels/distributions/tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions copying statsmodels/distributions/edgeworth.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions copying statsmodels/distributions/mixture_rvs.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions creating build/lib.linux-riscv64-cpython-314/statsmodels/interface copying statsmodels/interface/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/interface creating build/lib.linux-riscv64-cpython-314/statsmodels/robust copying statsmodels/robust/robust_linear_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/robust copying statsmodels/robust/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/robust copying statsmodels/robust/norms.py -> build/lib.linux-riscv64-cpython-314/statsmodels/robust copying statsmodels/robust/scale.py -> build/lib.linux-riscv64-cpython-314/statsmodels/robust creating build/lib.linux-riscv64-cpython-314/statsmodels/duration copying statsmodels/duration/hazard_regression.py -> build/lib.linux-riscv64-cpython-314/statsmodels/duration copying statsmodels/duration/_kernel_estimates.py -> build/lib.linux-riscv64-cpython-314/statsmodels/duration copying statsmodels/duration/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/duration copying statsmodels/duration/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/duration copying statsmodels/duration/survfunc.py -> build/lib.linux-riscv64-cpython-314/statsmodels/duration creating build/lib.linux-riscv64-cpython-314/statsmodels/compat copying statsmodels/compat/pandas.py -> build/lib.linux-riscv64-cpython-314/statsmodels/compat copying statsmodels/compat/patsy.py -> build/lib.linux-riscv64-cpython-314/statsmodels/compat copying statsmodels/compat/scipy.py -> build/lib.linux-riscv64-cpython-314/statsmodels/compat copying statsmodels/compat/_scipy_multivariate_t.py -> build/lib.linux-riscv64-cpython-314/statsmodels/compat copying statsmodels/compat/pytest.py -> build/lib.linux-riscv64-cpython-314/statsmodels/compat copying statsmodels/compat/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/compat copying statsmodels/compat/python.py -> build/lib.linux-riscv64-cpython-314/statsmodels/compat copying statsmodels/compat/platform.py -> build/lib.linux-riscv64-cpython-314/statsmodels/compat copying statsmodels/compat/numpy.py -> build/lib.linux-riscv64-cpython-314/statsmodels/compat creating build/lib.linux-riscv64-cpython-314/statsmodels/formula copying statsmodels/formula/formulatools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/formula copying statsmodels/formula/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/formula copying statsmodels/formula/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/formula creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox copying statsmodels/sandbox/infotheo.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox copying statsmodels/sandbox/mle.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox copying statsmodels/sandbox/sysreg.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox copying statsmodels/sandbox/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox copying statsmodels/sandbox/descstats.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox copying statsmodels/sandbox/rls.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox copying statsmodels/sandbox/pca.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox copying statsmodels/sandbox/gam.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox copying statsmodels/sandbox/bspline.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox copying statsmodels/sandbox/multilinear.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox copying statsmodels/sandbox/predict_functional.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox creating build/lib.linux-riscv64-cpython-314/statsmodels/gam copying statsmodels/gam/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam copying statsmodels/gam/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam copying statsmodels/gam/gam_penalties.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam copying statsmodels/gam/smooth_basis.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam copying statsmodels/gam/generalized_additive_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam creating build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests copying statsmodels/nonparametric/tests/test_kernel_density.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests copying statsmodels/nonparametric/tests/test_asymmetric.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests copying statsmodels/nonparametric/tests/test_kernel_regression.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests copying statsmodels/nonparametric/tests/test_lowess.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests copying statsmodels/nonparametric/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests copying statsmodels/nonparametric/tests/test_kde.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests copying statsmodels/nonparametric/tests/test_bandwidths.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests copying statsmodels/nonparametric/tests/test_kernels.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results copying statsmodels/nonparametric/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests copying statsmodels/graphics/tests/test_correlation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests copying statsmodels/graphics/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests copying statsmodels/graphics/tests/test_regressionplots.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests copying statsmodels/graphics/tests/test_dotplot.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests copying statsmodels/graphics/tests/test_mosaicplot.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests copying statsmodels/graphics/tests/test_factorplots.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests copying statsmodels/graphics/tests/test_gofplots.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests copying statsmodels/graphics/tests/test_tsaplots.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests copying statsmodels/graphics/tests/test_functional.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests copying statsmodels/graphics/tests/test_agreement.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests copying statsmodels/graphics/tests/test_boxplots.py -> build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/imputation/tests copying statsmodels/imputation/tests/test_mice.py -> build/lib.linux-riscv64-cpython-314/statsmodels/imputation/tests copying statsmodels/imputation/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/imputation/tests copying statsmodels/imputation/tests/test_ros.py -> build/lib.linux-riscv64-cpython-314/statsmodels/imputation/tests copying statsmodels/imputation/tests/test_bayes_mi.py -> build/lib.linux-riscv64-cpython-314/statsmodels/imputation/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests copying statsmodels/miscmodels/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests copying statsmodels/miscmodels/tests/test_ordinal_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests copying statsmodels/miscmodels/tests/test_tmodel.py -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests copying statsmodels/miscmodels/tests/results_tmodel.py -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests copying statsmodels/miscmodels/tests/test_generic_mle.py -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests copying statsmodels/miscmodels/tests/test_poisson.py -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests/results copying statsmodels/miscmodels/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests/results copying statsmodels/miscmodels/tests/results/results_ordinal_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/sunspots copying statsmodels/datasets/sunspots/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/sunspots copying statsmodels/datasets/sunspots/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/sunspots creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/statecrime copying statsmodels/datasets/statecrime/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/statecrime copying statsmodels/datasets/statecrime/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/statecrime creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fertility copying statsmodels/datasets/fertility/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fertility copying statsmodels/datasets/fertility/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fertility creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/strikes copying statsmodels/datasets/strikes/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/strikes copying statsmodels/datasets/strikes/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/strikes creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/tests copying statsmodels/datasets/tests/test_data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/tests copying statsmodels/datasets/tests/test_utils.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/tests copying statsmodels/datasets/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/cpunish copying statsmodels/datasets/cpunish/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/cpunish copying statsmodels/datasets/cpunish/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/cpunish creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/co2 copying statsmodels/datasets/co2/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/co2 copying statsmodels/datasets/co2/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/co2 creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/interest_inflation copying statsmodels/datasets/interest_inflation/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/interest_inflation copying statsmodels/datasets/interest_inflation/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/interest_inflation creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/anes96 copying statsmodels/datasets/anes96/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/anes96 copying statsmodels/datasets/anes96/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/anes96 creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/danish_data copying statsmodels/datasets/danish_data/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/danish_data copying statsmodels/datasets/danish_data/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/danish_data creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/elnino copying statsmodels/datasets/elnino/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/elnino copying statsmodels/datasets/elnino/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/elnino creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/elec_equip copying statsmodels/datasets/elec_equip/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/elec_equip copying statsmodels/datasets/elec_equip/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/elec_equip creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/china_smoking copying statsmodels/datasets/china_smoking/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/china_smoking copying statsmodels/datasets/china_smoking/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/china_smoking creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/scotland copying statsmodels/datasets/scotland/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/scotland copying statsmodels/datasets/scotland/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/scotland creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/copper copying statsmodels/datasets/copper/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/copper copying statsmodels/datasets/copper/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/copper creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/star98 copying statsmodels/datasets/star98/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/star98 copying statsmodels/datasets/star98/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/star98 creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/spector copying statsmodels/datasets/spector/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/spector copying statsmodels/datasets/spector/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/spector creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/cancer copying statsmodels/datasets/cancer/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/cancer copying statsmodels/datasets/cancer/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/cancer creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/modechoice copying statsmodels/datasets/modechoice/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/modechoice copying statsmodels/datasets/modechoice/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/modechoice creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fair copying statsmodels/datasets/fair/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fair copying statsmodels/datasets/fair/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fair creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/nile copying statsmodels/datasets/nile/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/nile copying statsmodels/datasets/nile/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/nile creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/engel copying statsmodels/datasets/engel/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/engel copying statsmodels/datasets/engel/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/engel creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/committee copying statsmodels/datasets/committee/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/committee copying statsmodels/datasets/committee/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/committee creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/macrodata copying statsmodels/datasets/macrodata/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/macrodata copying statsmodels/datasets/macrodata/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/macrodata creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/grunfeld copying statsmodels/datasets/grunfeld/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/grunfeld copying statsmodels/datasets/grunfeld/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/grunfeld creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/longley copying statsmodels/datasets/longley/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/longley copying statsmodels/datasets/longley/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/longley creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/ccard copying statsmodels/datasets/ccard/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/ccard copying statsmodels/datasets/ccard/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/ccard creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/randhie copying statsmodels/datasets/randhie/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/randhie copying statsmodels/datasets/randhie/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/randhie creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/stackloss copying statsmodels/datasets/stackloss/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/stackloss copying statsmodels/datasets/stackloss/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/stackloss creating build/lib.linux-riscv64-cpython-314/statsmodels/datasets/heart copying statsmodels/datasets/heart/data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/heart copying statsmodels/datasets/heart/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/heart creating build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests copying statsmodels/genmod/tests/test_constrained.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests copying statsmodels/genmod/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests copying statsmodels/genmod/tests/gee_gaussian_simulation_check.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests copying statsmodels/genmod/tests/test_glm_weights.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests copying statsmodels/genmod/tests/test_gee_glm.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests copying statsmodels/genmod/tests/gee_simulation_check.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests copying statsmodels/genmod/tests/gee_poisson_simulation_check.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests copying statsmodels/genmod/tests/test_glm.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests copying statsmodels/genmod/tests/test_qif.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests copying statsmodels/genmod/tests/test_bayes_mixed_glm.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests copying statsmodels/genmod/tests/test_score_test.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests copying statsmodels/genmod/tests/test_gee.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests copying statsmodels/genmod/tests/gee_categorical_simulation_check.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families copying statsmodels/genmod/families/varfuncs.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families copying statsmodels/genmod/families/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families copying statsmodels/genmod/families/family.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families copying statsmodels/genmod/families/links.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families creating build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/glmnet_r_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/results_glm.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/glm_test_resids.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/gee_generate_tests.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/results_glm_poisson_weights.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/elastic_net_generate_tests.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/res_R_var_weight.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families/tests copying statsmodels/genmod/families/tests/test_family.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families/tests copying statsmodels/genmod/families/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families/tests copying statsmodels/genmod/families/tests/test_link.py -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/treatment/tests copying statsmodels/treatment/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/treatment/tests copying statsmodels/treatment/tests/test_teffects.py -> build/lib.linux-riscv64-cpython-314/statsmodels/treatment/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/treatment/tests/results copying statsmodels/treatment/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/treatment/tests/results copying statsmodels/treatment/tests/results/results_teffects.py -> build/lib.linux-riscv64-cpython-314/statsmodels/treatment/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests copying statsmodels/othermod/tests/test_beta.py -> build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests copying statsmodels/othermod/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests/results copying statsmodels/othermod/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests/results copying statsmodels/othermod/tests/results/results_betareg.py -> build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests copying statsmodels/regression/tests/test_glsar_gretl.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests copying statsmodels/regression/tests/test_glsar_stata.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests copying statsmodels/regression/tests/test_regression.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests copying statsmodels/regression/tests/test_processreg.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests copying statsmodels/regression/tests/test_lme.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests copying statsmodels/regression/tests/test_quantile_regression.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests copying statsmodels/regression/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests copying statsmodels/regression/tests/test_dimred.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests copying statsmodels/regression/tests/test_tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests copying statsmodels/regression/tests/test_rolling.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests copying statsmodels/regression/tests/test_theil.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests copying statsmodels/regression/tests/test_predict.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests copying statsmodels/regression/tests/test_robustcov.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests copying statsmodels/regression/tests/test_recursive_ls.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests copying statsmodels/regression/tests/test_cov.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/glmnet_r_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/lme_r_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/macro_gr_corc_stata.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/results_regression.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/results_macro_ols_robust.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/results_theil_textile.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/generate_lasso.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/results_grunfeld_ols_robust_cluster.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/results_quantile_regression.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/generate_lme.py -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_base.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_robust_compare.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_meta.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_anova.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_diagnostic.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_regularized_covariance.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_contrast.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_correlation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_oneway.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_panel_robustcov.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_proportion.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_influence.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_sandwich.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_tost.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_weightstats.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_contingency_tables.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_multivariate.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_descriptivestats.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_outliers_influence.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_deltacov.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_multi.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_corrpsd.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_knockoff.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_pairwise.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_groups_sw.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_rates_poisson.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_power.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_moment_helpers.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_diagnostic_other.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_tabledist.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_nonparametric.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_lilliefors.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_mediation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_statstools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_oaxaca.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_effectsize.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_anova_rm.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_inter_rater.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_qsturng.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_gof.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/tests/test_dist_dependant_measures.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng copying statsmodels/stats/libqsturng/make_tbls.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng copying statsmodels/stats/libqsturng/qsturng_.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng copying statsmodels/stats/libqsturng/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng creating build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/lilliefors_critical_value_simulation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/results_power.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/results_proportion.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/results_meta.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/results_multinomial_proportions.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/results_rates.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/results_panelrobust.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng/tests copying statsmodels/stats/libqsturng/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng/tests copying statsmodels/stats/libqsturng/tests/test_qsturng.py -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/base/tests copying statsmodels/base/tests/test_data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base/tests copying statsmodels/base/tests/test_penalized.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base/tests copying statsmodels/base/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base/tests copying statsmodels/base/tests/test_distributed_estimation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base/tests copying statsmodels/base/tests/test_predict.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base/tests copying statsmodels/base/tests/test_optimize.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base/tests copying statsmodels/base/tests/test_penalties.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base/tests copying statsmodels/base/tests/test_transform.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base/tests copying statsmodels/base/tests/test_screening.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base/tests copying statsmodels/base/tests/test_shrink_pickle.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base/tests copying statsmodels/base/tests/test_generic_methods.py -> build/lib.linux-riscv64-cpython-314/statsmodels/base/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests copying statsmodels/multivariate/tests/test_factor.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests copying statsmodels/multivariate/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests copying statsmodels/multivariate/tests/test_multivariate_ols.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests copying statsmodels/multivariate/tests/test_manova.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests copying statsmodels/multivariate/tests/test_cancorr.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests copying statsmodels/multivariate/tests/test_pca.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests copying statsmodels/multivariate/tests/test_ml_factor.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/factor_rotation copying statsmodels/multivariate/factor_rotation/_analytic_rotation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/factor_rotation copying statsmodels/multivariate/factor_rotation/_gpa_rotation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/factor_rotation copying statsmodels/multivariate/factor_rotation/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/factor_rotation copying statsmodels/multivariate/factor_rotation/_wrappers.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/factor_rotation creating build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/results copying statsmodels/multivariate/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/results copying statsmodels/multivariate/tests/results/datamlw.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/factor_rotation/tests copying statsmodels/multivariate/factor_rotation/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/factor_rotation/tests copying statsmodels/multivariate/factor_rotation/tests/test_rotation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/factor_rotation/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests copying statsmodels/discrete/tests/test_margins.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests copying statsmodels/discrete/tests/test_diagnostic.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests copying statsmodels/discrete/tests/test_constrained.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests copying statsmodels/discrete/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests copying statsmodels/discrete/tests/test_count_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests copying statsmodels/discrete/tests/test_discrete.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests copying statsmodels/discrete/tests/test_predict.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests copying statsmodels/discrete/tests/test_sandwich_cov.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests copying statsmodels/discrete/tests/test_truncated_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests copying statsmodels/discrete/tests/test_conditional.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/results_count_robust_cluster.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/results_predict.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/results_poisson_constrained.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/results_glm_logit_constrained.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/results_discrete.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/results_count_margins.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/results_truncated.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/results_truncated_st.py -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests copying statsmodels/iolib/tests/test_table.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests copying statsmodels/iolib/tests/test_table_econpy.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests copying statsmodels/iolib/tests/test_summary_old.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests copying statsmodels/iolib/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests copying statsmodels/iolib/tests/test_pickle.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests copying statsmodels/iolib/tests/test_summary2.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests copying statsmodels/iolib/tests/test_summary.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/results copying statsmodels/iolib/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/results copying statsmodels/iolib/tests/results/macrodata.py -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/test_sequences.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/test_decorators.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/test_parallel.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/test_data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/test_grouputils.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/test_catadd.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/test_linalg.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/test_docstring.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/test_numdiff.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/test_eval_measures.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/test_tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/test_rootfinding.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/test_testing.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/test_web.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests copying statsmodels/tools/tests/test_transform_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/tools/validation copying statsmodels/tools/validation/decorators.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/validation copying statsmodels/tools/validation/validation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/validation copying statsmodels/tools/validation/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/validation creating build/lib.linux-riscv64-cpython-314/statsmodels/tools/validation/tests copying statsmodels/tools/validation/tests/test_validation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/validation/tests copying statsmodels/tools/validation/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tools/validation/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests copying statsmodels/emplike/tests/test_anova.py -> build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests copying statsmodels/emplike/tests/test_regression.py -> build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests copying statsmodels/emplike/tests/test_aft.py -> build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests copying statsmodels/emplike/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests copying statsmodels/emplike/tests/test_origin.py -> build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests copying statsmodels/emplike/tests/test_descriptive.py -> build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests/results copying statsmodels/emplike/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests/results copying statsmodels/emplike/tests/results/el_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar copying statsmodels/tsa/vector_ar/irf.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar copying statsmodels/tsa/vector_ar/hypothesis_test_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar copying statsmodels/tsa/vector_ar/var_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar copying statsmodels/tsa/vector_ar/plotting.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar copying statsmodels/tsa/vector_ar/util.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar copying statsmodels/tsa/vector_ar/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar copying statsmodels/tsa/vector_ar/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar copying statsmodels/tsa/vector_ar/output.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar copying statsmodels/tsa/vector_ar/vecm.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar copying statsmodels/tsa/vector_ar/svar_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/_quarterly_ar1.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/mlemodel.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/structural.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/kalman_filter.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/dynamic_factor_mq.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/news.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/_pykalman_smoother.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/varmax.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/simulation_smoother.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/cfa_simulation_smoother.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/sarimax.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/exponential_smoothing.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/initialization.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/representation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/kalman_smoother.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace copying statsmodels/tsa/statespace/dynamic_factor.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/interp copying statsmodels/tsa/interp/denton.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/interp copying statsmodels/tsa/interp/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/interp creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima copying statsmodels/tsa/arima/specification.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima copying statsmodels/tsa/arima/params.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima copying statsmodels/tsa/arima/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima copying statsmodels/tsa/arima/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima copying statsmodels/tsa/arima/model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima copying statsmodels/tsa/arima/tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching copying statsmodels/tsa/regime_switching/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching copying statsmodels/tsa/regime_switching/markov_autoregression.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching copying statsmodels/tsa/regime_switching/markov_switching.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching copying statsmodels/tsa/regime_switching/markov_regression.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests copying statsmodels/tsa/tests/test_deterministic.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests copying statsmodels/tsa/tests/test_adfuller_lag.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests copying statsmodels/tsa/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests copying statsmodels/tsa/tests/test_x13.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests copying statsmodels/tsa/tests/test_tsa_tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests copying statsmodels/tsa/tests/test_bds.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests copying statsmodels/tsa/tests/test_seasonal.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests copying statsmodels/tsa/tests/test_stattools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests copying statsmodels/tsa/tests/test_exponential_smoothing.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests copying statsmodels/tsa/tests/test_ar.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests copying statsmodels/tsa/tests/test_arima_process.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters copying statsmodels/tsa/holtwinters/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters copying statsmodels/tsa/holtwinters/model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters copying statsmodels/tsa/holtwinters/_smoothers.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters copying statsmodels/tsa/holtwinters/results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl copying statsmodels/tsa/ardl/pss_critical_values.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl copying statsmodels/tsa/ardl/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl copying statsmodels/tsa/ardl/model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl copying statsmodels/tsa/stl/mstl.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl copying statsmodels/tsa/stl/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base copying statsmodels/tsa/base/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base copying statsmodels/tsa/base/prediction.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base copying statsmodels/tsa/base/datetools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base copying statsmodels/tsa/base/tsa_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/forecasting copying statsmodels/tsa/forecasting/stl.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/forecasting copying statsmodels/tsa/forecasting/theta.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/forecasting copying statsmodels/tsa/forecasting/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/forecasting creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations copying statsmodels/tsa/innovations/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations copying statsmodels/tsa/innovations/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations copying statsmodels/tsa/innovations/arma_innovations.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/exponential_smoothing copying statsmodels/tsa/exponential_smoothing/ets.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/exponential_smoothing copying statsmodels/tsa/exponential_smoothing/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/exponential_smoothing copying statsmodels/tsa/exponential_smoothing/base.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/exponential_smoothing copying statsmodels/tsa/exponential_smoothing/initialization.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/exponential_smoothing creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters copying statsmodels/tsa/filters/filtertools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters copying statsmodels/tsa/filters/_utils.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters copying statsmodels/tsa/filters/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters copying statsmodels/tsa/filters/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters copying statsmodels/tsa/filters/hp_filter.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters copying statsmodels/tsa/filters/cf_filter.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters copying statsmodels/tsa/filters/bk_filter.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests copying statsmodels/tsa/vector_ar/tests/example_svar.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests copying statsmodels/tsa/vector_ar/tests/test_vecm.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests copying statsmodels/tsa/vector_ar/tests/test_svar.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests copying statsmodels/tsa/vector_ar/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests copying statsmodels/tsa/vector_ar/tests/test_var.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests copying statsmodels/tsa/vector_ar/tests/test_coint.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests copying statsmodels/tsa/vector_ar/tests/test_var_jmulti.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/Matlab_results copying statsmodels/tsa/vector_ar/tests/Matlab_results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/Matlab_results creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results copying statsmodels/tsa/vector_ar/tests/results/results_svar_st.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results copying statsmodels/tsa/vector_ar/tests/results/results_var.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results copying statsmodels/tsa/vector_ar/tests/results/results_svar.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results copying statsmodels/tsa/vector_ar/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results copying statsmodels/tsa/vector_ar/tests/results/results_var_data.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_var_output.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_vecm_output.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_varmax.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_collapsed.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_sarimax.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_prediction.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_news.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_dynamic_factor.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_representation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_decompose.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_weights.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_forecasting.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_impulse_responses.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_smoothing.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_simulate.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_structural.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_pickle.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_cfa_simulation_smoothing.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_fixed_params.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_kalman.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_mlemodel.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_chandrasekhar.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_options.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_var.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_exponential_smoothing.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_save.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_initialization.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_concentrated.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_cfa_tvpvar.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_univariate.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_models.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_simulation_smoothing.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/test_conserve_memory.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests copying statsmodels/tsa/statespace/tests/kfas_helpers.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters copying statsmodels/tsa/statespace/_filters/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers copying statsmodels/tsa/statespace/_smoothers/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_sarimax.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_kalman_filter.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_dynamic_factor.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_var_misc.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_structural.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_var_R.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_varmax.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/interp/tests copying statsmodels/tsa/interp/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/interp/tests copying statsmodels/tsa/interp/tests/test_denton.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/interp/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators copying statsmodels/tsa/arima/estimators/durbin_levinson.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators copying statsmodels/tsa/arima/estimators/innovations.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators copying statsmodels/tsa/arima/estimators/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators copying statsmodels/tsa/arima/estimators/yule_walker.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators copying statsmodels/tsa/arima/estimators/gls.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators copying statsmodels/tsa/arima/estimators/statespace.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators copying statsmodels/tsa/arima/estimators/hannan_rissanen.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators copying statsmodels/tsa/arima/estimators/burg.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/tests copying statsmodels/tsa/arima/tests/test_model.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/tests copying statsmodels/tsa/arima/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/tests copying statsmodels/tsa/arima/tests/test_params.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/tests copying statsmodels/tsa/arima/tests/test_tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/tests copying statsmodels/tsa/arima/tests/test_specification.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets copying statsmodels/tsa/arima/datasets/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests copying statsmodels/tsa/arima/estimators/tests/test_hannan_rissanen.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests copying statsmodels/tsa/arima/estimators/tests/test_statespace.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests copying statsmodels/tsa/arima/estimators/tests/test_innovations.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests copying statsmodels/tsa/arima/estimators/tests/test_burg.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests copying statsmodels/tsa/arima/estimators/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests copying statsmodels/tsa/arima/estimators/tests/test_durbin_levinson.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests copying statsmodels/tsa/arima/estimators/tests/test_gls.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests copying statsmodels/tsa/arima/estimators/tests/test_yule_walker.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets/brockwell_davis_2002 copying statsmodels/tsa/arima/datasets/brockwell_davis_2002/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets/brockwell_davis_2002 creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data copying statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/oshorts.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data copying statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/dowj.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data copying statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data copying statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/sbl.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data copying statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/lake.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests copying statsmodels/tsa/regime_switching/tests/test_markov_regression.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests copying statsmodels/tsa/regime_switching/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests copying statsmodels/tsa/regime_switching/tests/test_markov_autoregression.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests copying statsmodels/tsa/regime_switching/tests/test_markov_switching.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests/results copying statsmodels/tsa/regime_switching/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/arima111_css_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/results_arma_acf.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/arima211nc_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/results_ar.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/arima111_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/arima211nc_css_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/savedrvs.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/make_arma.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/arima211_css_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/arima112_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/results_arima.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/arima111nc_css_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/arima111nc_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/datamlw_tls.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/arima112nc_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/results_arma.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/results_process.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/arima112_css_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/arima211_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/arima112nc_css_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/tests copying statsmodels/tsa/holtwinters/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/tests copying statsmodels/tsa/holtwinters/tests/test_holtwinters.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/tests/results copying statsmodels/tsa/holtwinters/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl/tests copying statsmodels/tsa/ardl/tests/test_ardl.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl/tests copying statsmodels/tsa/ardl/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl/_pss_critical_values copying statsmodels/tsa/ardl/_pss_critical_values/pss.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl/_pss_critical_values copying statsmodels/tsa/ardl/_pss_critical_values/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl/_pss_critical_values copying statsmodels/tsa/ardl/_pss_critical_values/pss-process.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl/_pss_critical_values creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests copying statsmodels/tsa/stl/tests/test_stl.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests copying statsmodels/tsa/stl/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests copying statsmodels/tsa/stl/tests/test_mstl.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests/results copying statsmodels/tsa/stl/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/tests copying statsmodels/tsa/base/tests/test_base.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/tests copying statsmodels/tsa/base/tests/test_prediction.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/tests copying statsmodels/tsa/base/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/tests copying statsmodels/tsa/base/tests/test_datetools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/tests copying statsmodels/tsa/base/tests/test_tsa_indexes.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/forecasting/tests copying statsmodels/tsa/forecasting/tests/test_stl.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/forecasting/tests copying statsmodels/tsa/forecasting/tests/test_theta.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/forecasting/tests copying statsmodels/tsa/forecasting/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/forecasting/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations/tests copying statsmodels/tsa/innovations/tests/test_arma_innovations.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations/tests copying statsmodels/tsa/innovations/tests/test_cython_arma_innovations_fast.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations/tests copying statsmodels/tsa/innovations/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/tests copying statsmodels/tsa/filters/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/tests copying statsmodels/tsa/filters/tests/test_filters.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/tests/results copying statsmodels/tsa/filters/tests/results/filter_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/tests/results copying statsmodels/tsa/filters/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula copying statsmodels/distributions/copula/_special.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula copying statsmodels/distributions/copula/archimedean.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula copying statsmodels/distributions/copula/depfunc_ev.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula copying statsmodels/distributions/copula/elliptical.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula copying statsmodels/distributions/copula/extreme_value.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula copying statsmodels/distributions/copula/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula copying statsmodels/distributions/copula/api.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula copying statsmodels/distributions/copula/other_copulas.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula copying statsmodels/distributions/copula/transforms.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula copying statsmodels/distributions/copula/copulas.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula creating build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tests copying statsmodels/distributions/tests/test_edgeworth.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tests copying statsmodels/distributions/tests/test_mixture.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tests copying statsmodels/distributions/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tests copying statsmodels/distributions/tests/test_tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tests copying statsmodels/distributions/tests/test_discrete.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tests copying statsmodels/distributions/tests/test_ecdf.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tests copying statsmodels/distributions/tests/test_bernstein.py -> build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests copying statsmodels/robust/tests/test_norms.py -> build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests copying statsmodels/robust/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests copying statsmodels/robust/tests/test_scale.py -> build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests copying statsmodels/robust/tests/test_rlm.py -> build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests copying statsmodels/robust/tests/test_mquantiles.py -> build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests/results copying statsmodels/robust/tests/results/results_rlm.py -> build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests/results copying statsmodels/robust/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests/results copying statsmodels/robust/tests/results/results_norms.py -> build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests copying statsmodels/duration/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests copying statsmodels/duration/tests/test_phreg.py -> build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests copying statsmodels/duration/tests/test_survfunc.py -> build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results copying statsmodels/duration/tests/results/survival_enet_r_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results copying statsmodels/duration/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results copying statsmodels/duration/tests/results/phreg_gentests.py -> build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results copying statsmodels/duration/tests/results/survival_r_results.py -> build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results creating build/lib.linux-riscv64-cpython-314/statsmodels/compat/tests copying statsmodels/compat/tests/test_pandas.py -> build/lib.linux-riscv64-cpython-314/statsmodels/compat/tests copying statsmodels/compat/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/compat/tests copying statsmodels/compat/tests/test_itercompat.py -> build/lib.linux-riscv64-cpython-314/statsmodels/compat/tests copying statsmodels/compat/tests/test_scipy_compat.py -> build/lib.linux-riscv64-cpython-314/statsmodels/compat/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/formula/tests copying statsmodels/formula/tests/test_formula.py -> build/lib.linux-riscv64-cpython-314/statsmodels/formula/tests copying statsmodels/formula/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/formula/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric copying statsmodels/sandbox/nonparametric/kernels.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric copying statsmodels/sandbox/nonparametric/densityorthopoly.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric copying statsmodels/sandbox/nonparametric/kernel_extras.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric copying statsmodels/sandbox/nonparametric/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric copying statsmodels/sandbox/nonparametric/smoothers.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric copying statsmodels/sandbox/nonparametric/dgp_examples.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric copying statsmodels/sandbox/nonparametric/testdata.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric copying statsmodels/sandbox/nonparametric/kdecovclass.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric copying statsmodels/sandbox/nonparametric/kde2.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tests copying statsmodels/sandbox/tests/test_predict_functional.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tests copying statsmodels/sandbox/tests/savervs.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tests copying statsmodels/sandbox/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tests copying statsmodels/sandbox/tests/test_pca.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tests copying statsmodels/sandbox/tests/maketests_mlabwrap.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tests copying statsmodels/sandbox/tests/test_gam.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/archive copying statsmodels/sandbox/archive/linalg_covmat.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/archive copying statsmodels/sandbox/archive/tsa.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/archive copying statsmodels/sandbox/archive/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/archive copying statsmodels/sandbox/archive/linalg_decomp_1.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/archive creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/mcevaluate copying statsmodels/sandbox/mcevaluate/arma.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/mcevaluate copying statsmodels/sandbox/mcevaluate/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/mcevaluate creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/example_kernridge.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/penalized.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/predstd.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/try_treewalker.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/try_ols_anova.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/treewalkerclass.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/ar_panel.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/sympy_diff.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/runmnl.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/ols_anova_original.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/kernridgeregress_class.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/try_catdata.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/onewaygls.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/gmm.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression copying statsmodels/sandbox/regression/anova_nistcertified.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats copying statsmodels/sandbox/stats/stats_dhuard.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats copying statsmodels/sandbox/stats/multicomp.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats copying statsmodels/sandbox/stats/contrast_tools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats copying statsmodels/sandbox/stats/diagnostic.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats copying statsmodels/sandbox/stats/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats copying statsmodels/sandbox/stats/stats_mstats_short.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats copying statsmodels/sandbox/stats/runs.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats copying statsmodels/sandbox/stats/ex_newtests.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tools copying statsmodels/sandbox/tools/mctools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tools copying statsmodels/sandbox/tools/tools_pca.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tools copying statsmodels/sandbox/tools/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tools copying statsmodels/sandbox/tools/cross_val.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tools copying statsmodels/sandbox/tools/try_mctools.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tools creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel copying statsmodels/sandbox/panel/correlation_structures.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel copying statsmodels/sandbox/panel/random_panel.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel copying statsmodels/sandbox/panel/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel copying statsmodels/sandbox/panel/mixed.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel copying statsmodels/sandbox/panel/sandwich_covariance_generic.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel copying statsmodels/sandbox/panel/panel_short.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel copying statsmodels/sandbox/panel/panelmod.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa copying statsmodels/sandbox/tsa/example_arma.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa copying statsmodels/sandbox/tsa/fftarma.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa copying statsmodels/sandbox/tsa/varma.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa copying statsmodels/sandbox/tsa/try_fi.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa copying statsmodels/sandbox/tsa/diffusion2.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa copying statsmodels/sandbox/tsa/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa copying statsmodels/sandbox/tsa/try_var_convolve.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa copying statsmodels/sandbox/tsa/try_arma_more.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa copying statsmodels/sandbox/tsa/movstat.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa copying statsmodels/sandbox/tsa/diffusion.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions copying statsmodels/sandbox/distributions/otherdist.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions copying statsmodels/sandbox/distributions/genpareto.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions copying statsmodels/sandbox/distributions/mv_normal.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions copying statsmodels/sandbox/distributions/estimators.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions copying statsmodels/sandbox/distributions/transform_functions.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions copying statsmodels/sandbox/distributions/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions copying statsmodels/sandbox/distributions/quantize.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions copying statsmodels/sandbox/distributions/sppatch.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions copying statsmodels/sandbox/distributions/try_pot.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions copying statsmodels/sandbox/distributions/transformed.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions copying statsmodels/sandbox/distributions/multivariate.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions copying statsmodels/sandbox/distributions/gof_new.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions copying statsmodels/sandbox/distributions/extras.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions copying statsmodels/sandbox/distributions/mv_measures.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions copying statsmodels/sandbox/distributions/try_max.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/datarich copying statsmodels/sandbox/datarich/factormodels.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/datarich copying statsmodels/sandbox/datarich/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/datarich creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/tests copying statsmodels/sandbox/nonparametric/tests/ex_gam_new.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/tests copying statsmodels/sandbox/nonparametric/tests/ex_gam_am_new.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/tests copying statsmodels/sandbox/nonparametric/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/tests copying statsmodels/sandbox/nonparametric/tests/ex_smoothers.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/tests copying statsmodels/sandbox/nonparametric/tests/test_smoothers.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/tests copying statsmodels/sandbox/nonparametric/tests/test_kernel_extras.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests copying statsmodels/sandbox/regression/tests/results_gmm_griliches_iter.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests copying statsmodels/sandbox/regression/tests/test_gmm.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests copying statsmodels/sandbox/regression/tests/results_gmm_poisson.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests copying statsmodels/sandbox/regression/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests copying statsmodels/sandbox/regression/tests/results_gmm_griliches.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests copying statsmodels/sandbox/regression/tests/test_gmm_poisson.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests copying statsmodels/sandbox/regression/tests/results_ivreg2_griliches.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats/tests copying statsmodels/sandbox/stats/tests/test_runs.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats/tests copying statsmodels/sandbox/stats/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats/tests copying statsmodels/sandbox/stats/tests/test_multicomp.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel/tests copying statsmodels/sandbox/panel/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel/tests copying statsmodels/sandbox/panel/tests/test_random_panel.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests copying statsmodels/sandbox/distributions/tests/distparams.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests copying statsmodels/sandbox/distributions/tests/_est_fit.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests copying statsmodels/sandbox/distributions/tests/test_transf.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests copying statsmodels/sandbox/distributions/tests/check_moments.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests copying statsmodels/sandbox/distributions/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests copying statsmodels/sandbox/distributions/tests/test_multivariate.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests copying statsmodels/sandbox/distributions/tests/test_extras.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests copying statsmodels/sandbox/distributions/tests/test_gof_new.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests copying statsmodels/sandbox/distributions/tests/test_norm_expan.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/examples copying statsmodels/sandbox/distributions/examples/matchdist.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/examples copying statsmodels/sandbox/distributions/examples/ex_fitfr.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/examples copying statsmodels/sandbox/distributions/examples/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/examples copying statsmodels/sandbox/distributions/examples/ex_mvelliptical.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/examples copying statsmodels/sandbox/distributions/examples/ex_gof.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/examples copying statsmodels/sandbox/distributions/examples/ex_extras.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/examples copying statsmodels/sandbox/distributions/examples/ex_transf2.py -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/examples creating build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests copying statsmodels/gam/tests/test_penalized.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests copying statsmodels/gam/tests/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests copying statsmodels/gam/tests/test_smooth_basis.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests copying statsmodels/gam/tests/test_gam.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests creating build/lib.linux-riscv64-cpython-314/statsmodels/gam/gam_cross_validation copying statsmodels/gam/gam_cross_validation/gam_cross_validation.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/gam_cross_validation copying statsmodels/gam/gam_cross_validation/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/gam_cross_validation copying statsmodels/gam/gam_cross_validation/cross_validators.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/gam_cross_validation creating build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results copying statsmodels/gam/tests/results/results_mpg_bs_poisson.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results copying statsmodels/gam/tests/results/__init__.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results copying statsmodels/gam/tests/results/results_pls.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results copying statsmodels/gam/tests/results/results_mpg_bs.py -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results copying statsmodels/LICENSE.txt -> build/lib.linux-riscv64-cpython-314/statsmodels copying statsmodels/setup.cfg -> build/lib.linux-riscv64-cpython-314/statsmodels copying statsmodels/nonparametric/tests/results/test_lowess_simple.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results copying statsmodels/nonparametric/tests/results/results_kcde.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results copying statsmodels/nonparametric/tests/results/results_kde_univ_weights.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results copying statsmodels/nonparametric/tests/results/results_kde_fft.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results copying statsmodels/nonparametric/tests/results/results_kernel_regression.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results copying statsmodels/nonparametric/tests/results/results_kde.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results copying statsmodels/nonparametric/tests/results/results_kde_weights.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results copying statsmodels/nonparametric/tests/results/test_lowess_delta.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results copying statsmodels/nonparametric/tests/results/test_lowess_frac.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results copying statsmodels/nonparametric/tests/results/test_lowess_iter.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results copying statsmodels/miscmodels/tests/results/ologit_ucla.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests/results copying statsmodels/datasets/sunspots/sunspots.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/sunspots copying statsmodels/datasets/statecrime/statecrime.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/statecrime copying statsmodels/datasets/fertility/fertility.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fertility copying statsmodels/datasets/strikes/strikes.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/strikes copying statsmodels/datasets/cpunish/cpunish.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/cpunish copying statsmodels/datasets/co2/co2.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/co2 copying statsmodels/datasets/interest_inflation/E6_jmulti.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/interest_inflation copying statsmodels/datasets/interest_inflation/E6.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/interest_inflation copying statsmodels/datasets/anes96/anes96.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/anes96 copying statsmodels/datasets/danish_data/data.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/danish_data copying statsmodels/datasets/elnino/elnino.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/elnino copying statsmodels/datasets/elec_equip/elec_equip.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/elec_equip copying statsmodels/datasets/china_smoking/china_smoking.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/china_smoking copying statsmodels/datasets/scotland/scotvote.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/scotland copying statsmodels/datasets/copper/copper.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/copper copying statsmodels/datasets/star98/star98.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/star98 copying statsmodels/datasets/spector/spector.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/spector copying statsmodels/datasets/cancer/cancer.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/cancer copying statsmodels/datasets/modechoice/modechoice.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/modechoice copying statsmodels/datasets/fair/fair_pt.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fair copying statsmodels/datasets/fair/fair.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fair copying statsmodels/datasets/nile/nile.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/nile copying statsmodels/datasets/engel/engel.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/engel copying statsmodels/datasets/committee/committee.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/committee copying statsmodels/datasets/macrodata/macrodata.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/macrodata copying statsmodels/datasets/macrodata/macrodata.dta -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/macrodata copying statsmodels/datasets/grunfeld/grunfeld.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/grunfeld copying statsmodels/datasets/longley/longley.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/longley copying statsmodels/datasets/ccard/ccard.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/ccard copying statsmodels/datasets/randhie/randhie.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/randhie copying statsmodels/datasets/stackloss/stackloss.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/stackloss copying statsmodels/datasets/heart/heart.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/datasets/heart copying statsmodels/genmod/tests/results/enet_binomial.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/gee_ordinal_1.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/enet_poisson.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/stata_lbw_glm.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/results_tweedie_aweights_nonrobust.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/gee_poisson_1.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/gee_nominal_1.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/stata_cancer_glm.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/stata_medpar1_glm.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/medparlogresids.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/igaussident_resids.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/inv_gaussian.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/epil.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/gee_linear_1.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/iris.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/gee_nested_linear_1.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/genmod/tests/results/gee_logistic_1.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results copying statsmodels/treatment/tests/results/cataneo2.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/treatment/tests/results copying statsmodels/othermod/tests/results/resid_methylation.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests/results copying statsmodels/othermod/tests/results/foodexpenditure.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests/results copying statsmodels/othermod/tests/results/methylation-test.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests/results copying statsmodels/regression/tests/results/lme04.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/lasso_data.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/lme11.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/dietox.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/lme07.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/theil_textile_predict.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/lme06.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/lme10.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/lme09.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/lme02.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/lme03.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/lme00.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/lme08.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/results_rls_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/pastes.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/lme05.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/results_rls_stata.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/lme01.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/regression/tests/results/leverage_influence_ols_nostars.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results copying statsmodels/stats/tests/test_data.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests copying statsmodels/stats/libqsturng/CH.r -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng copying statsmodels/stats/libqsturng/LICENSE.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng copying statsmodels/stats/tests/results/wspec2.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/contingency_table_r_results.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/wspec3.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/influence_measures_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/influence_measures_bool_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/wspec4.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/bootleg.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/wspec1.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/framing.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/binary_constrict.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/results_influence_logit.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/influence_lsdiag_R.json -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/tests/results/data.dat -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results copying statsmodels/stats/libqsturng/tests/bootleg.dat -> build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng/tests copying statsmodels/multivariate/tests/results/factor_data.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/results copying statsmodels/multivariate/tests/results/factors_stata.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/results copying statsmodels/discrete/tests/results/nbinom_resids.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/ships.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/yhat_mnlogit.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/mnlogit_resid.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/sm3533.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/poisson_resid.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/phat_mnlogit.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/predict_prob_poisson.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/yhat_poisson.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/discrete/tests/results/mn_logit_summary.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results copying statsmodels/iolib/tests/results/time_series_examples.dta -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/results copying statsmodels/iolib/tests/results/data_missing.dta -> build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/results copying statsmodels/tsa/vector_ar/tests/Matlab_results/test_coint.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/Matlab_results copying statsmodels/tsa/vector_ar/tests/results/vars_results.npz -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results copying statsmodels/tsa/vector_ar/tests/results/e2.dat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results copying statsmodels/tsa/vector_ar/tests/results/e3.dat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results copying statsmodels/tsa/vector_ar/tests/results/e4.dat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results copying statsmodels/tsa/vector_ar/tests/results/e6.dat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results copying statsmodels/tsa/vector_ar/tests/results/e5.dat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results copying statsmodels/tsa/vector_ar/tests/results/e1.dat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_inst_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_granger_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_granger_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons_realgdp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_inst_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_granger_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realgdp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realgdp_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_granger_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_granger_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_granger_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_inst_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realgdp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons_realgdp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_granger_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_granger_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_inst_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_inst_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realgdp_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realgdp_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_granger_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realgdp_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_granger_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_inst_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_inst_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_granger_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons_realgdp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons_realgdp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_granger_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realgdp_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_inst_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_granger_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_inst_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_granger_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_inst_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_granger_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_inst_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons_realgdp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_inst_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_inst_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_inst_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_inst_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_granger_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realgdp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_granger_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_granger_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realgdp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_inst_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realgdp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_inst_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_inst_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons_realgdp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_granger_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realgdp_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_granger_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_inst_causality_dp_r.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_fc5.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realinv.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_diag.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realgdp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_lagorder.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_ir.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_inst_causality_r_dp.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_Sigmau.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs.txt -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Si0.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_S10.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_exact_initial_dfm_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_smoothing_generalobscov_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing2.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_state_variates.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing1.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_wpi1_ar3_stata.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_dynamic_factor_stata.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_invP.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_exact_initial_common_level_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_wpi1_missing_ar3_matlab_ssm.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_wpi1_ar3_matlab_ssm.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_sarimax_coverage.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_var_R_output.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/clark1989.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_exact_initial_common_level_restricted_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_measurement_error_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_v10.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing0.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing6.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_exact_initial_local_level_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_vi0.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_exact_initial_local_linear_trend_missing_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Omega_11.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_clark1989_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_mixed_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_missing_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_smoothing2_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Omega_22.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_posterior_mean.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_smoothing3_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/exponential_smoothing_predict.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/exponential_smoothing_params.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing4.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_varmax_stata.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_smoothing_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing3.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing5.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing3_variates.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/exponential_smoothing_states.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_intercepts_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_var_stata.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_realgdpar_stata.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_exact_initial_local_linear_trend_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_beta.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_R.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/manufac.dta -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/sm-0.9-sarimax.pkl -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_112.mat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_221.mat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_22F.mat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_11F.mat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_111.mat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_222.mat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_blocks_222.mat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_111.mat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_222.mat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_221.mat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_112.mat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_112.mat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_222.mat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_blocks_112.mat -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/2016-07-29.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/2016-06-29.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US copying statsmodels/tsa/regime_switching/tests/results/results_predict_fedfunds.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests/results copying statsmodels/tsa/regime_switching/tests/results/results_predict_rgnp.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests/results copying statsmodels/tsa/regime_switching/tests/results/mar_filardo.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests/results copying statsmodels/tsa/tests/results/results_arima_forecasts_all_mle_diff.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/arima111_forecasts.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/y_arma_data.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/bds_data.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/yhat_exact_nc.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/results_corrgram.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/yhat_css_nc.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/resids_exact_c.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/results_arma_forecasts.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/results_arima_forecasts_all_css.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/arima212_forecast.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/rand10000.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/resids_css_c.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/yhat_css_c.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/results_arima_forecasts_all_css_diff.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/results_arima_forecasts.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/results_arima_exog_forecasts_css.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/rgnp.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/results_arima_forecasts_all_mle.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/resids_exact_nc.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/AROLSConstantPredict.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/bds_results.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/results_ar_forecast_mle_dynamic.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/stkprc.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/yhat_exact_c.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/results_ccf.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/ARMLEConstantPredict.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/rgnpq.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/AROLSNoConstantPredict.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/results_arima_exog_forecasts_mle.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/resids_css_nc.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/gnpdef.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/lutkepohl2.dta -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/fit_ets_results_nonseasonal.json -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/fit_ets_results.json -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/tests/results/fit_ets_results_seasonal.json -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results copying statsmodels/tsa/holtwinters/tests/results/housing-data.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/tests/results copying statsmodels/tsa/stl/tests/results/stl_co2.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests/results copying statsmodels/tsa/stl/tests/results/mstl_elec_vic.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests/results copying statsmodels/tsa/stl/tests/results/mstl_test_results.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests/results copying statsmodels/tsa/stl/tests/results/stl_test_results.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests/results copying statsmodels/duration/tests/results/survival_data_50_2.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results copying statsmodels/duration/tests/results/survival_data_20_1.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results copying statsmodels/duration/tests/results/bmt.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results copying statsmodels/duration/tests/results/survival_data_100_5.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results copying statsmodels/duration/tests/results/survival_data_1000_10.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results copying statsmodels/duration/tests/results/survival_data_50_1.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results copying statsmodels/duration/tests/results/bmt_results.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results copying statsmodels/sandbox/regression/tests/griliches76.dta -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests copying statsmodels/sandbox/regression/tests/racd10data_with_transformed.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests copying statsmodels/gam/tests/results/autos_predict.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results copying statsmodels/gam/tests/results/autos.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results copying statsmodels/gam/tests/results/gam_PIRLS_results.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results copying statsmodels/gam/tests/results/cubic_cyclic_splines_from_mgcv.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results copying statsmodels/gam/tests/results/logit_gam_mgcv.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results copying statsmodels/gam/tests/results/autos_exog.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results copying statsmodels/gam/tests/results/prediction_from_mgcv.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results copying statsmodels/gam/tests/results/motorcycle.csv -> build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results running build_ext building 'statsmodels.tsa.stl._stl' extension creating build/temp.linux-riscv64-cpython-314/statsmodels/tsa/stl gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/stl/_stl.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/stl/_stl.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/stl/_stl.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/_stl.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.holtwinters._exponential_smoothers' extension creating build/temp.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/holtwinters/_exponential_smoothers.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/_exponential_smoothers.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/_exponential_smoothers.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/_exponential_smoothers.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.exponential_smoothing._ets_smooth' extension creating build/temp.linux-riscv64-cpython-314/statsmodels/tsa/exponential_smoothing gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/exponential_smoothing/_ets_smooth.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/exponential_smoothing/_ets_smooth.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/exponential_smoothing/_ets_smooth.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/exponential_smoothing/_ets_smooth.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa._innovations' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/_innovations.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/_innovations.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/_innovations.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/_innovations.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.regime_switching._hamilton_filter' extension creating build/temp.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/regime_switching/_hamilton_filter.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/_hamilton_filter.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/_hamilton_filter.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/_hamilton_filter.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.regime_switching._kim_smoother' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/regime_switching/_kim_smoother.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/_kim_smoother.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/_kim_smoother.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/_kim_smoother.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.innovations._arma_innovations' extension creating build/temp.linux-riscv64-cpython-314/statsmodels/tsa/innovations gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/innovations/_arma_innovations.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/innovations/_arma_innovations.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/innovations/_arma_innovations.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations/_arma_innovations.cpython-314-riscv64-linux-gnu.so building 'statsmodels.nonparametric.linbin' extension creating build/temp.linux-riscv64-cpython-314/statsmodels/nonparametric gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/nonparametric/linbin.c -o build/temp.linux-riscv64-cpython-314/statsmodels/nonparametric/linbin.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/nonparametric/linbin.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/linbin.cpython-314-riscv64-linux-gnu.so building 'statsmodels.robust._qn' extension creating build/temp.linux-riscv64-cpython-314/statsmodels/robust gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/robust/_qn.c -o build/temp.linux-riscv64-cpython-314/statsmodels/robust/_qn.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/robust/_qn.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/robust/_qn.cpython-314-riscv64-linux-gnu.so building 'statsmodels.nonparametric._smoothers_lowess' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/nonparametric/_smoothers_lowess.c -o build/temp.linux-riscv64-cpython-314/statsmodels/nonparametric/_smoothers_lowess.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/nonparametric/_smoothers_lowess.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/_smoothers_lowess.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._initialization' extension creating build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_initialization.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_initialization.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_initialization.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_initialization.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._representation' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_representation.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_representation.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_representation.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_representation.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._kalman_filter' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_kalman_filter.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_kalman_filter.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_kalman_filter.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_kalman_filter.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._filters._conventional' extension creating build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_filters/_conventional.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_conventional.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_conventional.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_conventional.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._filters._inversions' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_filters/_inversions.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_inversions.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_inversions.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_inversions.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._filters._univariate' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_filters/_univariate.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_univariate.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_univariate.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_univariate.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._filters._univariate_diffuse' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_filters/_univariate_diffuse.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_univariate_diffuse.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_univariate_diffuse.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_univariate_diffuse.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._kalman_smoother' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_kalman_smoother.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_kalman_smoother.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_kalman_smoother.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_kalman_smoother.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._smoothers._alternative' extension creating build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_smoothers/_alternative.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_alternative.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_alternative.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_alternative.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._smoothers._classical' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_smoothers/_classical.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_classical.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_classical.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_classical.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._smoothers._conventional' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_smoothers/_conventional.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_conventional.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_conventional.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_conventional.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._smoothers._univariate' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_smoothers/_univariate.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_univariate.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_univariate.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_univariate.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._smoothers._univariate_diffuse' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_smoothers/_univariate_diffuse.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_univariate_diffuse.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_univariate_diffuse.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_univariate_diffuse.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._simulation_smoother' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_simulation_smoother.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_simulation_smoother.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_simulation_smoother.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_simulation_smoother.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._cfa_simulation_smoother' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_cfa_simulation_smoother.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_cfa_simulation_smoother.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_cfa_simulation_smoother.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_cfa_simulation_smoother.cpython-314-riscv64-linux-gnu.so building 'statsmodels.tsa.statespace._tools' extension gcc -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto -fPIC -DCYTHON_TRACE_NOGIL=0 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -Istatsmodels/src -I/usr/lib/python3.14/site-packages/numpy/_core/include -I/usr/include/python3.14 -c statsmodels/tsa/statespace/_tools.c -o build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_tools.o gcc -shared -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -Wl,-O1 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -flto=auto -march=rv64gc -mabi=lp64d -O2 -pipe -fno-plt -fexceptions -Wp,-D_FORTIFY_SOURCE=3 -Wformat -Werror=format-security -fstack-clash-protection -fno-omit-frame-pointer -g -ffile-prefix-map=/build/python-statsmodels/src=/usr/src/debug/python-statsmodels -flto=auto build/temp.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_tools.o -L/usr/lib/python3.14/site-packages/numpy/_core/include/../lib -L/usr/lib -o build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_tools.cpython-314-riscv64-linux-gnu.so installing to build/bdist.linux-riscv64/wheel running install running install_lib creating build/bdist.linux-riscv64/wheel creating build/bdist.linux-riscv64/wheel/statsmodels creating build/bdist.linux-riscv64/wheel/statsmodels/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/kernels.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/kernels_asymmetric.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric creating build/bdist.linux-riscv64/wheel/statsmodels/nonparametric/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/test_kernel_density.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/test_asymmetric.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/test_kernel_regression.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/test_lowess.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/test_kde.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests creating build/bdist.linux-riscv64/wheel/statsmodels/nonparametric/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results/test_lowess_simple.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results/results_kcde.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results/results_kde_univ_weights.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results/results_kde_fft.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results/results_kernel_regression.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results/results_kde.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results/results_kde_weights.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results/test_lowess_delta.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results/test_lowess_frac.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/results/test_lowess_iter.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/test_bandwidths.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/tests/test_kernels.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/linbin.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/kernel_density.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/smoothers_lowess.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/kde.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/kdetools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/bandwidths.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/_kernel_base.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/smoothers_lowess_old.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/_smoothers_lowess.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/kernel_regression.py -> build/bdist.linux-riscv64/wheel/./statsmodels/nonparametric creating build/bdist.linux-riscv64/wheel/statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/correlation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/functional.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics creating build/bdist.linux-riscv64/wheel/statsmodels/graphics/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests/test_correlation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests/test_regressionplots.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests/test_dotplot.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests/test_mosaicplot.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests/test_factorplots.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests/test_gofplots.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests/test_tsaplots.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests/test_functional.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests/test_agreement.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests/test_boxplots.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/factorplots.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tsaplots.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/dotplots.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/gofplots.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/plottools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tukeyplot.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/boxplots.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/_regressionplots_doc.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/utils.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/mosaicplot.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/agreement.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/regressionplots.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics copying build/lib.linux-riscv64-cpython-314/statsmodels/graphics/plot_grids.py -> build/bdist.linux-riscv64/wheel/./statsmodels/graphics creating build/bdist.linux-riscv64/wheel/statsmodels/imputation creating build/bdist.linux-riscv64/wheel/statsmodels/imputation/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/imputation/tests/test_mice.py -> build/bdist.linux-riscv64/wheel/./statsmodels/imputation/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/imputation/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/imputation/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/imputation/tests/test_ros.py -> build/bdist.linux-riscv64/wheel/./statsmodels/imputation/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/imputation/tests/test_bayes_mi.py -> build/bdist.linux-riscv64/wheel/./statsmodels/imputation/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/imputation/mice.py -> build/bdist.linux-riscv64/wheel/./statsmodels/imputation copying build/lib.linux-riscv64-cpython-314/statsmodels/imputation/ros.py -> build/bdist.linux-riscv64/wheel/./statsmodels/imputation copying build/lib.linux-riscv64-cpython-314/statsmodels/imputation/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/imputation copying build/lib.linux-riscv64-cpython-314/statsmodels/imputation/bayes_mi.py -> build/bdist.linux-riscv64/wheel/./statsmodels/imputation creating build/bdist.linux-riscv64/wheel/statsmodels/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tests/test_package.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tests/test_x13.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tests creating build/bdist.linux-riscv64/wheel/statsmodels/miscmodels copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/try_mlecov.py -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels creating build/bdist.linux-riscv64/wheel/statsmodels/miscmodels/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels/tests creating build/bdist.linux-riscv64/wheel/statsmodels/miscmodels/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests/results/results_ordinal_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests/results/ologit_ucla.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests/test_ordinal_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests/test_tmodel.py -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests/results_tmodel.py -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests/test_generic_mle.py -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tests/test_poisson.py -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/count.py -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/nonlinls.py -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/ordinal_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels copying build/lib.linux-riscv64-cpython-314/statsmodels/miscmodels/tmodel.py -> build/bdist.linux-riscv64/wheel/./statsmodels/miscmodels creating build/bdist.linux-riscv64/wheel/statsmodels/datasets copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/template_data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/sunspots copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/sunspots/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/sunspots copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/sunspots/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/sunspots copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/sunspots/sunspots.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/sunspots creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/statecrime copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/statecrime/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/statecrime copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/statecrime/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/statecrime copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/statecrime/statecrime.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/statecrime creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/fertility copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fertility/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/fertility copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fertility/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/fertility copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fertility/fertility.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/fertility creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/strikes copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/strikes/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/strikes copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/strikes/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/strikes copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/strikes/strikes.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/strikes creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/tests/test_data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/tests/test_utils.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/tests creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/cpunish copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/cpunish/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/cpunish copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/cpunish/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/cpunish copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/cpunish/cpunish.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/cpunish creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/co2 copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/co2/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/co2 copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/co2/co2.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/co2 copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/co2/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/co2 creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/interest_inflation copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/interest_inflation/E6_jmulti.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/interest_inflation copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/interest_inflation/E6.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/interest_inflation copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/interest_inflation/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/interest_inflation copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/interest_inflation/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/interest_inflation creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/anes96 copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/anes96/anes96.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/anes96 copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/anes96/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/anes96 copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/anes96/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/anes96 creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/danish_data copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/danish_data/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/danish_data copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/danish_data/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/danish_data copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/danish_data/data.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/danish_data creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/elnino copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/elnino/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/elnino copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/elnino/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/elnino copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/elnino/elnino.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/elnino creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/elec_equip copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/elec_equip/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/elec_equip copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/elec_equip/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/elec_equip copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/elec_equip/elec_equip.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/elec_equip creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/china_smoking copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/china_smoking/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/china_smoking copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/china_smoking/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/china_smoking copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/china_smoking/china_smoking.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/china_smoking copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/scotland copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/scotland/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/scotland copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/scotland/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/scotland copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/scotland/scotvote.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/scotland creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/copper copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/copper/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/copper copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/copper/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/copper copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/copper/copper.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/copper creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/star98 copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/star98/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/star98 copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/star98/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/star98 copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/star98/star98.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/star98 creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/spector copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/spector/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/spector copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/spector/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/spector copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/spector/spector.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/spector creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/cancer copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/cancer/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/cancer copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/cancer/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/cancer copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/cancer/cancer.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/cancer creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/modechoice copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/modechoice/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/modechoice copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/modechoice/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/modechoice copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/modechoice/modechoice.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/modechoice creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/fair copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fair/fair_pt.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/fair copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fair/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/fair copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fair/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/fair copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/fair/fair.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/fair creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/nile copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/nile/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/nile copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/nile/nile.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/nile copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/nile/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/nile creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/engel copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/engel/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/engel copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/engel/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/engel copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/engel/engel.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/engel creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/committee copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/committee/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/committee copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/committee/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/committee copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/committee/committee.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/committee creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/macrodata copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/macrodata/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/macrodata copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/macrodata/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/macrodata copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/macrodata/macrodata.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/macrodata copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/macrodata/macrodata.dta -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/macrodata creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/grunfeld copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/grunfeld/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/grunfeld copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/grunfeld/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/grunfeld copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/grunfeld/grunfeld.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/grunfeld creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/longley copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/longley/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/longley copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/longley/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/longley copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/longley/longley.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/longley creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/ccard copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/ccard/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/ccard copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/ccard/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/ccard copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/ccard/ccard.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/ccard creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/randhie copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/randhie/randhie.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/randhie copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/randhie/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/randhie copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/randhie/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/randhie creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/stackloss copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/stackloss/stackloss.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/stackloss copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/stackloss/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/stackloss copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/stackloss/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/stackloss copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/utils.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets creating build/bdist.linux-riscv64/wheel/statsmodels/datasets/heart copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/heart/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/heart copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/heart/heart.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/heart copying build/lib.linux-riscv64-cpython-314/statsmodels/datasets/heart/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/datasets/heart creating build/bdist.linux-riscv64/wheel/statsmodels/genmod creating build/bdist.linux-riscv64/wheel/statsmodels/genmod/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/test_constrained.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/gee_gaussian_simulation_check.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/test_glm_weights.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests creating build/bdist.linux-riscv64/wheel/statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/enet_binomial.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/glmnet_r_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/gee_ordinal_1.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/results_glm.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/enet_poisson.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/stata_lbw_glm.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/results_tweedie_aweights_nonrobust.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/gee_poisson_1.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/gee_nominal_1.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/stata_cancer_glm.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/glm_test_resids.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/gee_generate_tests.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/stata_medpar1_glm.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/medparlogresids.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/igaussident_resids.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/inv_gaussian.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/epil.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/results_glm_poisson_weights.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/gee_linear_1.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/iris.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/gee_nested_linear_1.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/elastic_net_generate_tests.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/gee_logistic_1.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/results/res_R_var_weight.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/test_gee_glm.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/gee_simulation_check.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/gee_poisson_simulation_check.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/test_glm.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/test_qif.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/test_bayes_mixed_glm.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/test_score_test.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/test_gee.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/tests/gee_categorical_simulation_check.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/tests creating build/bdist.linux-riscv64/wheel/statsmodels/genmod/families copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families/varfuncs.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/families creating build/bdist.linux-riscv64/wheel/statsmodels/genmod/families/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families/tests/test_family.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/families/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/families/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families/tests/test_link.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/families/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/families copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families/family.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/families copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families/links.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod/families copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/bayes_mixed_glm.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/generalized_linear_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/_tweedie_compound_poisson.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/cov_struct.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/generalized_estimating_equations.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod copying build/lib.linux-riscv64-cpython-314/statsmodels/genmod/qif.py -> build/bdist.linux-riscv64/wheel/./statsmodels/genmod copying build/lib.linux-riscv64-cpython-314/statsmodels/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels creating build/bdist.linux-riscv64/wheel/statsmodels/treatment creating build/bdist.linux-riscv64/wheel/statsmodels/treatment/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/treatment/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/treatment/tests creating build/bdist.linux-riscv64/wheel/statsmodels/treatment/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/treatment/tests/results/cataneo2.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/treatment/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/treatment/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/treatment/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/treatment/tests/results/results_teffects.py -> build/bdist.linux-riscv64/wheel/./statsmodels/treatment/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/treatment/tests/test_teffects.py -> build/bdist.linux-riscv64/wheel/./statsmodels/treatment/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/treatment/treatment_effects.py -> build/bdist.linux-riscv64/wheel/./statsmodels/treatment copying build/lib.linux-riscv64-cpython-314/statsmodels/treatment/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/treatment creating build/bdist.linux-riscv64/wheel/statsmodels/othermod creating build/bdist.linux-riscv64/wheel/statsmodels/othermod/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests/test_beta.py -> build/bdist.linux-riscv64/wheel/./statsmodels/othermod/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/othermod/tests creating build/bdist.linux-riscv64/wheel/statsmodels/othermod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests/results/resid_methylation.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/othermod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/othermod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests/results/foodexpenditure.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/othermod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests/results/results_betareg.py -> build/bdist.linux-riscv64/wheel/./statsmodels/othermod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/othermod/tests/results/methylation-test.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/othermod/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/othermod/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/othermod copying build/lib.linux-riscv64-cpython-314/statsmodels/othermod/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/othermod copying build/lib.linux-riscv64-cpython-314/statsmodels/othermod/betareg.py -> build/bdist.linux-riscv64/wheel/./statsmodels/othermod creating build/bdist.linux-riscv64/wheel/statsmodels/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/recursive_ls.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/_tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/rolling.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression creating build/bdist.linux-riscv64/wheel/statsmodels/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/test_glsar_gretl.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/test_glsar_stata.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/test_regression.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/test_processreg.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/test_lme.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/test_quantile_regression.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/test_dimred.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/test_tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/test_rolling.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/test_theil.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests creating build/bdist.linux-riscv64/wheel/statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/lme04.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/lasso_data.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/glmnet_r_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/lme_r_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/macro_gr_corc_stata.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/results_regression.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/lme11.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/results_macro_ols_robust.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/results_theil_textile.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/dietox.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/lme07.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/generate_lasso.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/theil_textile_predict.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/lme06.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/lme10.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/lme09.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/lme02.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/lme03.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/lme00.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/lme08.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/results_grunfeld_ols_robust_cluster.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/results_rls_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/results_quantile_regression.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/pastes.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/generate_lme.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/lme05.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/results_rls_stata.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/lme01.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/results/leverage_influence_ols_nostars.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/test_predict.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/test_robustcov.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/test_recursive_ls.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/tests/test_cov.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/linear_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/dimred.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/mixed_linear_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/_prediction.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/process_regression.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/feasible_gls.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/regression/quantile_regression.py -> build/bdist.linux-riscv64/wheel/./statsmodels/regression creating build/bdist.linux-riscv64/wheel/statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/dist_dependence_measures.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/_inference_tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/diagnostic_gen.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tabledist.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/anova.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/robust_compare.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/sandwich_covariance.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/multicomp.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/regularized_covariance.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats creating build/bdist.linux-riscv64/wheel/statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_base.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_robust_compare.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_meta.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_anova.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_diagnostic.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_regularized_covariance.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_contrast.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_correlation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_oneway.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_panel_robustcov.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_proportion.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_data.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_influence.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_sandwich.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_tost.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_weightstats.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_contingency_tables.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_multivariate.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_descriptivestats.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_outliers_influence.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_deltacov.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_multi.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests creating build/bdist.linux-riscv64/wheel/statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/lilliefors_critical_value_simulation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/wspec2.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/data.dat -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/contingency_table_r_results.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/results_power.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/influence_lsdiag_R.json -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/wspec3.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/results_proportion.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/influence_measures_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/results_meta.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/influence_measures_bool_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/wspec4.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/results_multinomial_proportions.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/bootleg.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/results_rates.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/wspec1.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/framing.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/binary_constrict.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/results_panelrobust.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/results/results_influence_logit.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_corrpsd.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_knockoff.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_pairwise.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_groups_sw.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_rates_poisson.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_power.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_moment_helpers.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_diagnostic_other.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_tabledist.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_nonparametric.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_lilliefors.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_mediation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_statstools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_oaxaca.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_effectsize.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_anova_rm.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_inter_rater.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_qsturng.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_gof.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/tests/test_dist_dependant_measures.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/outliers_influence.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/correlation_tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/_lilliefors.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/_lilliefors_critical_values.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/diagnostic.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/gof.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/_diagnostic_other.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/inter_rater.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/proportion.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/power.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/_delta_method.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/oaxaca.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/meta_analysis.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/rates.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/contingency_tables.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/knockoff_regeffects.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/base.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats creating build/bdist.linux-riscv64/wheel/statsmodels/stats/libqsturng copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng/make_tbls.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/libqsturng creating build/bdist.linux-riscv64/wheel/statsmodels/stats/libqsturng/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/libqsturng/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng/tests/bootleg.dat -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/libqsturng/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng/tests/test_qsturng.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/libqsturng/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng/qsturng_.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/libqsturng copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/libqsturng copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng/CH.r -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/libqsturng copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/libqsturng/LICENSE.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/stats/libqsturng copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/descriptivestats.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/nonparametric.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/effect_size.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/multivariate.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/weightstats.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/oneway.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/mediation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/moment_helpers.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/_knockoff.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/multitest.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/multivariate_tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/_adnorm.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/stattools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/stats/contrast.py -> build/bdist.linux-riscv64/wheel/./statsmodels/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/LICENSE.txt -> build/bdist.linux-riscv64/wheel/./statsmodels copying build/lib.linux-riscv64-cpython-314/statsmodels/_version.py -> build/bdist.linux-riscv64/wheel/./statsmodels copying build/lib.linux-riscv64-cpython-314/statsmodels/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels creating build/bdist.linux-riscv64/wheel/statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_slsqp.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/covtype.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base creating build/bdist.linux-riscv64/wheel/statsmodels/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/base/tests/test_data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/base/tests/test_penalized.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/base/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/base/tests/test_distributed_estimation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/base/tests/test_predict.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/base/tests/test_optimize.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/base/tests/test_penalties.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/base/tests/test_transform.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/base/tests/test_screening.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/base/tests/test_shrink_pickle.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/base/tests/test_generic_methods.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/base/elastic_net.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/transform.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/_penalized.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_solvers_common.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/_parameter_inference.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/_penalties.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/_screening.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/_prediction_inference.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/_constraints.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/optimizer.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/distributed_estimation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_cvxopt.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/base/wrapper.py -> build/bdist.linux-riscv64/wheel/./statsmodels/base copying build/lib.linux-riscv64-cpython-314/statsmodels/conftest.py -> build/bdist.linux-riscv64/wheel/./statsmodels creating build/bdist.linux-riscv64/wheel/statsmodels/multivariate copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/manova.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate creating build/bdist.linux-riscv64/wheel/statsmodels/multivariate/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/test_factor.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/tests creating build/bdist.linux-riscv64/wheel/statsmodels/multivariate/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/results/datamlw.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/results/factor_data.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/results/factors_stata.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/test_multivariate_ols.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/test_manova.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/test_cancorr.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/test_pca.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/tests/test_ml_factor.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/cancorr.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/plots.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate creating build/bdist.linux-riscv64/wheel/statsmodels/multivariate/factor_rotation copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/factor_rotation/_analytic_rotation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/factor_rotation creating build/bdist.linux-riscv64/wheel/statsmodels/multivariate/factor_rotation/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/factor_rotation/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/factor_rotation/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/factor_rotation/tests/test_rotation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/factor_rotation/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/factor_rotation/_gpa_rotation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/factor_rotation copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/factor_rotation/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/factor_rotation copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/factor_rotation/_wrappers.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate/factor_rotation copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/pca.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/multivariate_ols.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate copying build/lib.linux-riscv64-cpython-314/statsmodels/multivariate/factor.py -> build/bdist.linux-riscv64/wheel/./statsmodels/multivariate creating build/bdist.linux-riscv64/wheel/statsmodels/discrete copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/count_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete creating build/bdist.linux-riscv64/wheel/statsmodels/discrete/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/test_margins.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/test_diagnostic.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/test_constrained.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests creating build/bdist.linux-riscv64/wheel/statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/nbinom_resids.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/results_count_robust_cluster.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/ships.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/results_predict.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/yhat_mnlogit.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/mnlogit_resid.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/sm3533.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/results_poisson_constrained.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/results_glm_logit_constrained.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/results_discrete.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/results_count_margins.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/poisson_resid.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/phat_mnlogit.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/predict_prob_poisson.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/mn_logit_summary.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/results_truncated.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/results_truncated_st.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/results/yhat_poisson.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/test_count_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/test_discrete.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/test_predict.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/test_sandwich_cov.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/test_truncated_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/tests/test_conditional.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/diagnostic.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/conditional_models.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/discrete_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/discrete_margins.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/truncated_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete copying build/lib.linux-riscv64-cpython-314/statsmodels/discrete/_diagnostics_count.py -> build/bdist.linux-riscv64/wheel/./statsmodels/discrete creating build/bdist.linux-riscv64/wheel/statsmodels/src copying build/lib.linux-riscv64-cpython-314/statsmodels/src/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/src creating build/bdist.linux-riscv64/wheel/statsmodels/iolib creating build/bdist.linux-riscv64/wheel/statsmodels/iolib/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/test_table.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/test_table_econpy.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/test_summary_old.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/test_pickle.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib/tests creating build/bdist.linux-riscv64/wheel/statsmodels/iolib/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/results/time_series_examples.dta -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/results/data_missing.dta -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/results/macrodata.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/test_summary2.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tests/test_summary.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/smpickle.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/summary2.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/foreign.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/openfile.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/tableformatting.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/summary.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/table.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib copying build/lib.linux-riscv64-cpython-314/statsmodels/iolib/stata_summary_examples.py -> build/bdist.linux-riscv64/wheel/./statsmodels/iolib creating build/bdist.linux-riscv64/wheel/statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/decorators.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools creating build/bdist.linux-riscv64/wheel/statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/test_sequences.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/test_decorators.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/test_parallel.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/test_data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/test_grouputils.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/test_catadd.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/test_linalg.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/test_docstring.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/test_numdiff.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/test_eval_measures.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/test_tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/test_rootfinding.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/test_testing.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/test_web.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tests/test_transform_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/eval_measures.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/docstring.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools creating build/bdist.linux-riscv64/wheel/statsmodels/tools/validation copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/validation/decorators.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/validation copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/validation/validation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/validation creating build/bdist.linux-riscv64/wheel/statsmodels/tools/validation/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/validation/tests/test_validation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/validation/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/validation/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/validation/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/validation/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools/validation copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/sm_exceptions.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/linalg.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/sequences.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/web.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/print_version.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/grouputils.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/numdiff.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/typing.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/testing.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/catadd.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/_test_runner.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/_testing.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/parallel.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/rng_qrng.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/transform_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/tools/rootfinding.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tools creating build/bdist.linux-riscv64/wheel/statsmodels/emplike creating build/bdist.linux-riscv64/wheel/statsmodels/emplike/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests/test_anova.py -> build/bdist.linux-riscv64/wheel/./statsmodels/emplike/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests/test_regression.py -> build/bdist.linux-riscv64/wheel/./statsmodels/emplike/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests/test_aft.py -> build/bdist.linux-riscv64/wheel/./statsmodels/emplike/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/emplike/tests creating build/bdist.linux-riscv64/wheel/statsmodels/emplike/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/emplike/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests/results/el_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/emplike/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests/test_origin.py -> build/bdist.linux-riscv64/wheel/./statsmodels/emplike/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/emplike/tests/test_descriptive.py -> build/bdist.linux-riscv64/wheel/./statsmodels/emplike/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/emplike/descriptive.py -> build/bdist.linux-riscv64/wheel/./statsmodels/emplike copying build/lib.linux-riscv64-cpython-314/statsmodels/emplike/aft_el.py -> build/bdist.linux-riscv64/wheel/./statsmodels/emplike copying build/lib.linux-riscv64-cpython-314/statsmodels/emplike/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/emplike copying build/lib.linux-riscv64-cpython-314/statsmodels/emplike/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/emplike copying build/lib.linux-riscv64-cpython-314/statsmodels/emplike/originregress.py -> build/bdist.linux-riscv64/wheel/./statsmodels/emplike copying build/lib.linux-riscv64-cpython-314/statsmodels/emplike/elanova.py -> build/bdist.linux-riscv64/wheel/./statsmodels/emplike copying build/lib.linux-riscv64-cpython-314/statsmodels/emplike/elregress.py -> build/bdist.linux-riscv64/wheel/./statsmodels/emplike creating build/bdist.linux-riscv64/wheel/statsmodels/tsa creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/vector_ar copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/irf.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/vector_ar/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/example_svar.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/test_vecm.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/test_svar.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/vector_ar/tests/Matlab_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/Matlab_results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/Matlab_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/Matlab_results/test_coint.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/Matlab_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/vector_ar/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results/results_svar_st.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results/e2.dat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results/e3.dat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results/results_var.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results/results_svar.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results/e4.dat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results/results_var_data.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results/e6.dat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results/e5.dat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results/e1.dat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/results/vars_results.npz -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/test_var.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/test_coint.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_inst_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_granger_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_granger_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons_realgdp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_inst_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_granger_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realgdp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realgdp_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_granger_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_granger_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_granger_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_inst_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realgdp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons_realgdp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_granger_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_granger_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_inst_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_inst_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realgdp_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realgdp_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_granger_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realgdp_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_granger_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_inst_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_inst_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_granger_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons_realgdp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons_realgdp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_granger_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realgdp_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_inst_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_granger_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_inst_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_granger_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_inst_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_granger_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_inst_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons_realgdp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_inst_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_inst_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_inst_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_inst_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_granger_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realgdp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_granger_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_granger_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realgdp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_inst_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realgdp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_inst_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_inst_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_var_output.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons_realgdp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_granger_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_vecm_output.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realgdp_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_granger_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_inst_causality_dp_r.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_fc5.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realinv.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_diag.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realgdp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_lagorder.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_ir.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_inst_causality_r_dp.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_Sigmau.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs.txt -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/tests/test_var_jmulti.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/hypothesis_test_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/var_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/plotting.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/util.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/output.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/vecm.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/svar_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/vector_ar creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_simulation_smoother.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_quarterly_ar1.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_kalman_smoother.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_varmax.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_collapsed.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_sarimax.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_prediction.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_news.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_dynamic_factor.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_representation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_decompose.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_weights.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_forecasting.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_impulse_responses.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_smoothing.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_simulate.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_structural.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_pickle.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_cfa_simulation_smoothing.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_fixed_params.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_kalman.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_mlemodel.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Si0.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_S10.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_exact_initial_dfm_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_smoothing_generalobscov_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing2.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_state_variates.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_sarimax.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing1.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_wpi1_ar3_stata.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_kalman_filter.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_dynamic_factor_stata.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_invP.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/statespace/tests/results/frbny_nowcast copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_112.mat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_221.mat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/2016-07-29.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/2016-06-29.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_22F.mat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_11F.mat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_111.mat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_222.mat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_blocks_222.mat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_111.mat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_222.mat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_221.mat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_112.mat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_112.mat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_222.mat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_blocks_112.mat -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_exact_initial_common_level_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_wpi1_missing_ar3_matlab_ssm.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_wpi1_ar3_matlab_ssm.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_sarimax_coverage.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_var_R_output.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/manufac.dta -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/clark1989.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_exact_initial_common_level_restricted_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_measurement_error_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_v10.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing0.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing6.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_dynamic_factor.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_exact_initial_local_level_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_var_misc.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_vi0.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_exact_initial_local_linear_trend_missing_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Omega_11.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_clark1989_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_mixed_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_structural.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_missing_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_smoothing2_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Omega_22.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_posterior_mean.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_smoothing3_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/exponential_smoothing_predict.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/exponential_smoothing_params.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing4.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_varmax_stata.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_smoothing_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing3.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing5.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing3_variates.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/exponential_smoothing_states.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_var_R.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_intercepts_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_varmax.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_var_stata.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_realgdpar_stata.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_exact_initial_local_linear_trend_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_beta.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/sm-0.9-sarimax.pkl -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_R.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_chandrasekhar.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_options.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_var.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_save.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_initialization.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_concentrated.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_cfa_tvpvar.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_univariate.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_models.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_simulation_smoothing.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/test_conserve_memory.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tests/kfas_helpers.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/mlemodel.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/structural.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/kalman_filter.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_kalman_filter.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/dynamic_factor_mq.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/news.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_representation.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_pykalman_smoother.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_cfa_simulation_smoother.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/varmax.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_initialization.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/simulation_smoother.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/statespace/_filters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_univariate_diffuse.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/_filters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_conventional.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/_filters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/_filters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_inversions.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/_filters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_filters/_univariate.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/_filters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/cfa_simulation_smoother.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/sarimax.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/exponential_smoothing.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/statespace/_smoothers copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_univariate_diffuse.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/_smoothers copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_conventional.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/_smoothers copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/_smoothers copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_alternative.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/_smoothers copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_classical.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/_smoothers copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_smoothers/_univariate.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace/_smoothers copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/initialization.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/_tools.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/representation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/kalman_smoother.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/dynamic_factor.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/statespace creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/interp copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/interp/denton.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/interp creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/interp/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/interp/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/interp/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/interp/tests/test_denton.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/interp/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/interp/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/interp creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/arima creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/arima/estimators creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/arima/estimators/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests/test_hannan_rissanen.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests/test_statespace.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests/test_innovations.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests/test_burg.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests/test_durbin_levinson.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests/test_gls.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/tests/test_yule_walker.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/durbin_levinson.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/innovations.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/yule_walker.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/gls.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/statespace.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/hannan_rissanen.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/estimators/burg.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/estimators creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/arima/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/tests/test_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/tests/test_params.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/tests/test_tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/tests/test_specification.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/specification.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/arima/datasets copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/datasets creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/arima/datasets/brockwell_davis_2002 creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/oshorts.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002/data copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/dowj.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002/data copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002/data copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/sbl.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002/data copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/lake.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002/data copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/datasets/brockwell_davis_2002/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002 copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/params.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima/tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/arima creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/regime_switching copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/_kim_smoother.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/regime_switching creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/regime_switching/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests/test_markov_regression.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/regime_switching/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/regime_switching/tests creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/regime_switching/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/regime_switching/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests/results/results_predict_fedfunds.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/regime_switching/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests/results/results_predict_rgnp.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/regime_switching/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests/results/mar_filardo.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/regime_switching/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests/test_markov_autoregression.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/regime_switching/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/tests/test_markov_switching.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/regime_switching/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/regime_switching copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/markov_autoregression.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/regime_switching copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/markov_switching.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/regime_switching copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/_hamilton_filter.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/regime_switching copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/regime_switching/markov_regression.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/regime_switching creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/test_deterministic.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/test_adfuller_lag.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/test_x13.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/test_tsa_tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/test_bds.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_arima_forecasts_all_mle_diff.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/arima111_forecasts.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/arima111_css_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/y_arma_data.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/bds_data.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/yhat_exact_nc.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_corrgram.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/yhat_css_nc.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_arma_acf.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/arima211nc_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/resids_exact_c.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/fit_ets_results_nonseasonal.json -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_ar.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/arima111_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_arma_forecasts.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_arima_forecasts_all_css.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/arima212_forecast.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/rand10000.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/arima211nc_css_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/lutkepohl2.dta -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/resids_css_c.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/savedrvs.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/make_arma.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/arima211_css_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/yhat_css_c.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_arima_forecasts_all_css_diff.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/arima112_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_arima_forecasts.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_arima.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_arima_exog_forecasts_css.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/arima111nc_css_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/rgnp.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_arima_forecasts_all_mle.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/arima111nc_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/resids_exact_nc.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/AROLSConstantPredict.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/bds_results.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_ar_forecast_mle_dynamic.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/datamlw_tls.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/arima112nc_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/stkprc.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/fit_ets_results.json -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/yhat_exact_c.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_ccf.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/ARMLEConstantPredict.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_arma.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_process.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/arima112_css_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/rgnpq.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/AROLSNoConstantPredict.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/results_arima_exog_forecasts_mle.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/arima211_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/fit_ets_results_seasonal.json -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/resids_css_nc.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/arima112nc_css_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/results/gnpdef.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/test_seasonal.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/test_stattools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/test_exponential_smoothing.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/test_ar.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/test_arima_process.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/mlemodel.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/seasonal.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/holtwinters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/_exponential_smoothers.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/holtwinters creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/holtwinters/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/holtwinters/tests creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/holtwinters/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/tests/results/housing-data.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/holtwinters/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/holtwinters/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/tests/test_holtwinters.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/holtwinters/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/holtwinters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/holtwinters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/_smoothers.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/holtwinters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/holtwinters/results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/holtwinters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tsatools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/ardl copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl/pss_critical_values.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/ardl creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/ardl/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl/tests/test_ardl.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/ardl/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/ardl/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/ardl copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl/model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/ardl creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/ardl/_pss_critical_values copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl/_pss_critical_values/pss.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/ardl/_pss_critical_values copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl/_pss_critical_values/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/ardl/_pss_critical_values copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ardl/_pss_critical_values/pss-process.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/ardl/_pss_critical_values creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/stl copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/mstl.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/stl copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/_stl.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/stl creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/stl/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests/test_stl.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/stl/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/stl/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests/test_mstl.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/stl/tests creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/stl/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests/results/stl_co2.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/stl/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/stl/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests/results/mstl_elec_vic.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/stl/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests/results/mstl_test_results.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/stl/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/tests/results/stl_test_results.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/stl/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/stl copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arma_mle.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/coint_tables.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/ar_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/adfvalues.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/base creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/tests/test_base.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/tests/test_prediction.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/tests/test_datetools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/tests/test_tsa_indexes.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/base/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/base copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/prediction.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/base copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/datetools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/base copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/tsa_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/base creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/forecasting creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/forecasting/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/forecasting/tests/test_stl.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/forecasting/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/forecasting/tests/test_theta.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/forecasting/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/forecasting/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/forecasting/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/forecasting/stl.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/forecasting copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/forecasting/theta.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/forecasting copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/forecasting/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/forecasting creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/innovations creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/innovations/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations/tests/test_arma_innovations.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/innovations/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations/tests/test_cython_arma_innovations_fast.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/innovations/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/innovations/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/innovations copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/innovations copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations/arma_innovations.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/innovations copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/innovations/_arma_innovations.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/innovations creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/exponential_smoothing copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/exponential_smoothing/ets.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/exponential_smoothing copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/exponential_smoothing/_ets_smooth.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/exponential_smoothing copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/exponential_smoothing/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/exponential_smoothing copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/exponential_smoothing/base.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/exponential_smoothing copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/exponential_smoothing/initialization.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/exponential_smoothing creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/filters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/filtertools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/filters creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/filters/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/filters/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/tests/test_filters.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/filters/tests creating build/bdist.linux-riscv64/wheel/statsmodels/tsa/filters/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/tests/results/filter_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/filters/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/filters/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/_utils.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/filters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/filters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/filters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/hp_filter.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/filters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/cf_filter.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/filters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/filters/bk_filter.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa/filters copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/_innovations.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/arima_process.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/descriptivestats.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/deterministic.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/varma_process.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/_bds.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/x13.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stattools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/tsa creating build/bdist.linux-riscv64/wheel/statsmodels/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/bernstein.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions creating build/bdist.linux-riscv64/wheel/statsmodels/distributions/copula copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula/_special.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/copula copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula/archimedean.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/copula copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula/depfunc_ev.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/copula copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula/elliptical.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/copula copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula/extreme_value.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/copula copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/copula copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/copula copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula/other_copulas.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/copula copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula/transforms.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/copula copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/copula/copulas.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/copula creating build/bdist.linux-riscv64/wheel/statsmodels/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tests/test_edgeworth.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tests/test_mixture.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tests/test_tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tests/test_discrete.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tests/test_ecdf.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tests/test_bernstein.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/discrete.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/empirical_distribution.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/edgeworth.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/distributions/mixture_rvs.py -> build/bdist.linux-riscv64/wheel/./statsmodels/distributions creating build/bdist.linux-riscv64/wheel/statsmodels/interface copying build/lib.linux-riscv64-cpython-314/statsmodels/interface/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/interface creating build/bdist.linux-riscv64/wheel/statsmodels/robust copying build/lib.linux-riscv64-cpython-314/statsmodels/robust/_qn.cpython-314-riscv64-linux-gnu.so -> build/bdist.linux-riscv64/wheel/./statsmodels/robust creating build/bdist.linux-riscv64/wheel/statsmodels/robust/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests/test_norms.py -> build/bdist.linux-riscv64/wheel/./statsmodels/robust/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/robust/tests creating build/bdist.linux-riscv64/wheel/statsmodels/robust/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests/results/results_rlm.py -> build/bdist.linux-riscv64/wheel/./statsmodels/robust/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/robust/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests/results/results_norms.py -> build/bdist.linux-riscv64/wheel/./statsmodels/robust/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests/test_scale.py -> build/bdist.linux-riscv64/wheel/./statsmodels/robust/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests/test_rlm.py -> build/bdist.linux-riscv64/wheel/./statsmodels/robust/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/robust/tests/test_mquantiles.py -> build/bdist.linux-riscv64/wheel/./statsmodels/robust/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/robust/robust_linear_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/robust copying build/lib.linux-riscv64-cpython-314/statsmodels/robust/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/robust copying build/lib.linux-riscv64-cpython-314/statsmodels/robust/norms.py -> build/bdist.linux-riscv64/wheel/./statsmodels/robust copying build/lib.linux-riscv64-cpython-314/statsmodels/robust/scale.py -> build/bdist.linux-riscv64/wheel/./statsmodels/robust creating build/bdist.linux-riscv64/wheel/statsmodels/duration creating build/bdist.linux-riscv64/wheel/statsmodels/duration/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/duration/tests creating build/bdist.linux-riscv64/wheel/statsmodels/duration/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results/survival_enet_r_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/duration/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results/survival_data_50_2.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/duration/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results/survival_data_20_1.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/duration/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/duration/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results/bmt.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/duration/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results/survival_data_100_5.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/duration/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results/survival_data_1000_10.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/duration/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results/phreg_gentests.py -> build/bdist.linux-riscv64/wheel/./statsmodels/duration/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results/survival_data_50_1.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/duration/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results/bmt_results.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/duration/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/results/survival_r_results.py -> build/bdist.linux-riscv64/wheel/./statsmodels/duration/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/test_phreg.py -> build/bdist.linux-riscv64/wheel/./statsmodels/duration/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/tests/test_survfunc.py -> build/bdist.linux-riscv64/wheel/./statsmodels/duration/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/hazard_regression.py -> build/bdist.linux-riscv64/wheel/./statsmodels/duration copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/_kernel_estimates.py -> build/bdist.linux-riscv64/wheel/./statsmodels/duration copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/duration copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/duration copying build/lib.linux-riscv64-cpython-314/statsmodels/duration/survfunc.py -> build/bdist.linux-riscv64/wheel/./statsmodels/duration creating build/bdist.linux-riscv64/wheel/statsmodels/compat copying build/lib.linux-riscv64-cpython-314/statsmodels/compat/pandas.py -> build/bdist.linux-riscv64/wheel/./statsmodels/compat creating build/bdist.linux-riscv64/wheel/statsmodels/compat/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/compat/tests/test_pandas.py -> build/bdist.linux-riscv64/wheel/./statsmodels/compat/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/compat/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/compat/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/compat/tests/test_itercompat.py -> build/bdist.linux-riscv64/wheel/./statsmodels/compat/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/compat/tests/test_scipy_compat.py -> build/bdist.linux-riscv64/wheel/./statsmodels/compat/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/compat/patsy.py -> build/bdist.linux-riscv64/wheel/./statsmodels/compat copying build/lib.linux-riscv64-cpython-314/statsmodels/compat/scipy.py -> build/bdist.linux-riscv64/wheel/./statsmodels/compat copying build/lib.linux-riscv64-cpython-314/statsmodels/compat/_scipy_multivariate_t.py -> build/bdist.linux-riscv64/wheel/./statsmodels/compat copying build/lib.linux-riscv64-cpython-314/statsmodels/compat/pytest.py -> build/bdist.linux-riscv64/wheel/./statsmodels/compat copying build/lib.linux-riscv64-cpython-314/statsmodels/compat/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/compat copying build/lib.linux-riscv64-cpython-314/statsmodels/compat/python.py -> build/bdist.linux-riscv64/wheel/./statsmodels/compat copying build/lib.linux-riscv64-cpython-314/statsmodels/compat/platform.py -> build/bdist.linux-riscv64/wheel/./statsmodels/compat copying build/lib.linux-riscv64-cpython-314/statsmodels/compat/numpy.py -> build/bdist.linux-riscv64/wheel/./statsmodels/compat copying build/lib.linux-riscv64-cpython-314/statsmodels/setup.cfg -> build/bdist.linux-riscv64/wheel/./statsmodels creating build/bdist.linux-riscv64/wheel/statsmodels/formula copying build/lib.linux-riscv64-cpython-314/statsmodels/formula/formulatools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/formula creating build/bdist.linux-riscv64/wheel/statsmodels/formula/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/formula/tests/test_formula.py -> build/bdist.linux-riscv64/wheel/./statsmodels/formula/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/formula/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/formula/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/formula/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/formula copying build/lib.linux-riscv64-cpython-314/statsmodels/formula/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/formula creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/kernels.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/nonparametric creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/nonparametric/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/tests/ex_gam_new.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/nonparametric/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/tests/ex_gam_am_new.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/nonparametric/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/nonparametric/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/tests/ex_smoothers.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/nonparametric/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/tests/test_smoothers.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/nonparametric/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/tests/test_kernel_extras.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/nonparametric/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/densityorthopoly.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/kernel_extras.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/smoothers.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/dgp_examples.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/testdata.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/kdecovclass.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/nonparametric copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/nonparametric/kde2.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/nonparametric creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tests/test_predict_functional.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tests/savervs.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tests/test_pca.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tests/maketests_mlabwrap.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tests/test_gam.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/infotheo.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/mle.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/sysreg.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/archive copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/archive/linalg_covmat.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/archive copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/archive/tsa.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/archive copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/archive/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/archive copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/archive/linalg_decomp_1.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/archive copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/descstats.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/mcevaluate copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/mcevaluate/arma.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/mcevaluate copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/mcevaluate/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/mcevaluate creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/example_kernridge.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/penalized.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/predstd.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests/racd10data_with_transformed.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests/griliches76.dta -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests/results_gmm_griliches_iter.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests/test_gmm.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests/results_gmm_poisson.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests/results_gmm_griliches.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests/test_gmm_poisson.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tests/results_ivreg2_griliches.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/try_treewalker.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/try_ols_anova.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/treewalkerclass.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/ar_panel.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/sympy_diff.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/runmnl.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/ols_anova_original.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/kernridgeregress_class.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/try_catdata.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/onewaygls.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/gmm.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/regression/anova_nistcertified.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/regression creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats/stats_dhuard.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats/multicomp.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/stats creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats/tests/test_runs.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats/tests/test_multicomp.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/stats/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats/contrast_tools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats/diagnostic.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats/stats_mstats_short.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats/runs.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/stats copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/stats/ex_newtests.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/stats creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tools/mctools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tools/tools_pca.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tools/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tools/cross_val.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tools/try_mctools.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tools copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/rls.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/pca.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/gam.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/panel copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel/correlation_structures.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/panel creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/panel/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/panel/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel/tests/test_random_panel.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/panel/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel/random_panel.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/panel copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/panel copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel/mixed.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/panel copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel/sandwich_covariance_generic.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/panel copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel/panel_short.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/panel copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/panel/panelmod.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/panel creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa/example_arma.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa/fftarma.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa/varma.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa/try_fi.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa/diffusion2.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa/try_var_convolve.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa/try_arma_more.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa/movstat.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tsa copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/tsa/diffusion.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/tsa creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/otherdist.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests/distparams.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests/_est_fit.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests/test_transf.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests/check_moments.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests/test_multivariate.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests/test_extras.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests/test_gof_new.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/tests/test_norm_expan.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/genpareto.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/mv_normal.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/estimators.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/transform_functions.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/quantize.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/sppatch.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/distributions/examples copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/examples/matchdist.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/examples copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/examples/ex_fitfr.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/examples copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/examples/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/examples copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/examples/ex_mvelliptical.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/examples copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/examples/ex_gof.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/examples copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/examples/ex_extras.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/examples copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/examples/ex_transf2.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions/examples copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/try_pot.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/transformed.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/multivariate.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/gof_new.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/extras.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/mv_measures.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/distributions/try_max.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/distributions creating build/bdist.linux-riscv64/wheel/statsmodels/sandbox/datarich copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/datarich/factormodels.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/datarich copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/datarich/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox/datarich copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/bspline.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/multilinear.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox copying build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/predict_functional.py -> build/bdist.linux-riscv64/wheel/./statsmodels/sandbox creating build/bdist.linux-riscv64/wheel/statsmodels/gam creating build/bdist.linux-riscv64/wheel/statsmodels/gam/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/test_penalized.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/test_smooth_basis.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests creating build/bdist.linux-riscv64/wheel/statsmodels/gam/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results/autos_predict.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results/results_mpg_bs_poisson.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results/autos.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results/gam_PIRLS_results.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results/cubic_cyclic_splines_from_mgcv.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results/logit_gam_mgcv.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results/results_pls.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results/autos_exog.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results/prediction_from_mgcv.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results/results_mpg_bs.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/results/motorcycle.csv -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests/results copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/tests/test_gam.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/tests creating build/bdist.linux-riscv64/wheel/statsmodels/gam/gam_cross_validation copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/gam_cross_validation/gam_cross_validation.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/gam_cross_validation copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/gam_cross_validation/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/gam_cross_validation copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/gam_cross_validation/cross_validators.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam/gam_cross_validation copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/__init__.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/api.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/gam_penalties.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/smooth_basis.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam copying build/lib.linux-riscv64-cpython-314/statsmodels/gam/generalized_additive_model.py -> build/bdist.linux-riscv64/wheel/./statsmodels/gam running install_egg_info running egg_info writing statsmodels.egg-info/PKG-INFO writing dependency_links to statsmodels.egg-info/dependency_links.txt writing requirements to statsmodels.egg-info/requires.txt writing top-level names to statsmodels.egg-info/top_level.txt dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative dependency /usr/lib/python3.14/site-packages/numpy/_core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative reading manifest template 'MANIFEST.in' warning: no files found matching '*.pxi' anywhere in distribution warning: no previously-included files matching '*' found under directory 'dist' warning: no previously-included files found matching 'docs/source/generated/*' warning: no files found matching '*' under directory 'docs/sphinxext' warning: no files found matching '*' under directory 'docs/themes' warning: no previously-included files matching '*' found under directory 'docs/build' warning: no previously-included files matching '*' found under directory 'docs/build/htmlhelp' warning: no files found matching 'statsmodels/statsmodelsdoc.chm' no previously-included directories found matching '*/__pycache__' warning: no previously-included files matching '*~' found anywhere in distribution warning: no previously-included files matching '*.swp' found anywhere in distribution warning: no previously-included files matching '*.pyc' found anywhere in distribution warning: no previously-included files matching '*.pyo' found anywhere in distribution warning: no previously-included files matching '*.bak' found anywhere in distribution adding license file 'LICENSE.txt' writing manifest file 'statsmodels.egg-info/SOURCES.txt' Copying statsmodels.egg-info to build/bdist.linux-riscv64/wheel/./statsmodels-0.14.7.dev0+g40e6a84d2.d20260830-py3.14.egg-info running install_scripts creating build/bdist.linux-riscv64/wheel/statsmodels-0.14.7.dev0+g40e6a84d2.d20260830.dist-info/WHEEL creating '/build/python-statsmodels/src/statsmodels/dist/.tmp-yi6g9sew/statsmodels-0.14.7.dev0+g40e6a84d2.d20260830-cp314-cp314-linux_riscv64.whl' and adding 'build/bdist.linux-riscv64/wheel' to it adding 'statsmodels/LICENSE.txt' adding 'statsmodels/__init__.py' adding 'statsmodels/_version.py' adding 'statsmodels/api.py' adding 'statsmodels/conftest.py' adding 'statsmodels/setup.cfg' adding 'statsmodels/base/__init__.py' adding 'statsmodels/base/_constraints.py' adding 'statsmodels/base/_parameter_inference.py' adding 'statsmodels/base/_penalized.py' adding 'statsmodels/base/_penalties.py' adding 'statsmodels/base/_prediction_inference.py' adding 'statsmodels/base/_screening.py' adding 'statsmodels/base/covtype.py' adding 'statsmodels/base/data.py' adding 'statsmodels/base/distributed_estimation.py' adding 'statsmodels/base/elastic_net.py' adding 'statsmodels/base/l1_cvxopt.py' adding 'statsmodels/base/l1_slsqp.py' adding 'statsmodels/base/l1_solvers_common.py' adding 'statsmodels/base/model.py' adding 'statsmodels/base/optimizer.py' adding 'statsmodels/base/transform.py' adding 'statsmodels/base/wrapper.py' adding 'statsmodels/base/tests/__init__.py' adding 'statsmodels/base/tests/test_data.py' adding 'statsmodels/base/tests/test_distributed_estimation.py' adding 'statsmodels/base/tests/test_generic_methods.py' adding 'statsmodels/base/tests/test_optimize.py' adding 'statsmodels/base/tests/test_penalized.py' adding 'statsmodels/base/tests/test_penalties.py' adding 'statsmodels/base/tests/test_predict.py' adding 'statsmodels/base/tests/test_screening.py' adding 'statsmodels/base/tests/test_shrink_pickle.py' adding 'statsmodels/base/tests/test_transform.py' adding 'statsmodels/compat/__init__.py' adding 'statsmodels/compat/_scipy_multivariate_t.py' adding 'statsmodels/compat/numpy.py' adding 'statsmodels/compat/pandas.py' adding 'statsmodels/compat/patsy.py' adding 'statsmodels/compat/platform.py' adding 'statsmodels/compat/pytest.py' adding 'statsmodels/compat/python.py' adding 'statsmodels/compat/scipy.py' adding 'statsmodels/compat/tests/__init__.py' adding 'statsmodels/compat/tests/test_itercompat.py' adding 'statsmodels/compat/tests/test_pandas.py' adding 'statsmodels/compat/tests/test_scipy_compat.py' adding 'statsmodels/datasets/__init__.py' adding 'statsmodels/datasets/template_data.py' adding 'statsmodels/datasets/utils.py' adding 'statsmodels/datasets/anes96/__init__.py' adding 'statsmodels/datasets/anes96/anes96.csv' adding 'statsmodels/datasets/anes96/data.py' adding 'statsmodels/datasets/cancer/__init__.py' adding 'statsmodels/datasets/cancer/cancer.csv' adding 'statsmodels/datasets/cancer/data.py' adding 'statsmodels/datasets/ccard/__init__.py' adding 'statsmodels/datasets/ccard/ccard.csv' adding 'statsmodels/datasets/ccard/data.py' adding 'statsmodels/datasets/china_smoking/__init__.py' adding 'statsmodels/datasets/china_smoking/china_smoking.csv' adding 'statsmodels/datasets/china_smoking/data.py' adding 'statsmodels/datasets/co2/__init__.py' adding 'statsmodels/datasets/co2/co2.csv' adding 'statsmodels/datasets/co2/data.py' adding 'statsmodels/datasets/committee/__init__.py' adding 'statsmodels/datasets/committee/committee.csv' adding 'statsmodels/datasets/committee/data.py' adding 'statsmodels/datasets/copper/__init__.py' adding 'statsmodels/datasets/copper/copper.csv' adding 'statsmodels/datasets/copper/data.py' adding 'statsmodels/datasets/cpunish/__init__.py' adding 'statsmodels/datasets/cpunish/cpunish.csv' adding 'statsmodels/datasets/cpunish/data.py' adding 'statsmodels/datasets/danish_data/__init__.py' adding 'statsmodels/datasets/danish_data/data.csv' adding 'statsmodels/datasets/danish_data/data.py' adding 'statsmodels/datasets/elec_equip/__init__.py' adding 'statsmodels/datasets/elec_equip/data.py' adding 'statsmodels/datasets/elec_equip/elec_equip.csv' adding 'statsmodels/datasets/elnino/__init__.py' adding 'statsmodels/datasets/elnino/data.py' adding 'statsmodels/datasets/elnino/elnino.csv' adding 'statsmodels/datasets/engel/__init__.py' adding 'statsmodels/datasets/engel/data.py' adding 'statsmodels/datasets/engel/engel.csv' adding 'statsmodels/datasets/fair/__init__.py' adding 'statsmodels/datasets/fair/data.py' adding 'statsmodels/datasets/fair/fair.csv' adding 'statsmodels/datasets/fair/fair_pt.csv' adding 'statsmodels/datasets/fertility/__init__.py' adding 'statsmodels/datasets/fertility/data.py' adding 'statsmodels/datasets/fertility/fertility.csv' adding 'statsmodels/datasets/grunfeld/__init__.py' adding 'statsmodels/datasets/grunfeld/data.py' adding 'statsmodels/datasets/grunfeld/grunfeld.csv' adding 'statsmodels/datasets/heart/__init__.py' adding 'statsmodels/datasets/heart/data.py' adding 'statsmodels/datasets/heart/heart.csv' adding 'statsmodels/datasets/interest_inflation/E6.csv' adding 'statsmodels/datasets/interest_inflation/E6_jmulti.csv' adding 'statsmodels/datasets/interest_inflation/__init__.py' adding 'statsmodels/datasets/interest_inflation/data.py' adding 'statsmodels/datasets/longley/__init__.py' adding 'statsmodels/datasets/longley/data.py' adding 'statsmodels/datasets/longley/longley.csv' adding 'statsmodels/datasets/macrodata/__init__.py' adding 'statsmodels/datasets/macrodata/data.py' adding 'statsmodels/datasets/macrodata/macrodata.csv' adding 'statsmodels/datasets/macrodata/macrodata.dta' adding 'statsmodels/datasets/modechoice/__init__.py' adding 'statsmodels/datasets/modechoice/data.py' adding 'statsmodels/datasets/modechoice/modechoice.csv' adding 'statsmodels/datasets/nile/__init__.py' adding 'statsmodels/datasets/nile/data.py' adding 'statsmodels/datasets/nile/nile.csv' adding 'statsmodels/datasets/randhie/__init__.py' adding 'statsmodels/datasets/randhie/data.py' adding 'statsmodels/datasets/randhie/randhie.csv' adding 'statsmodels/datasets/scotland/__init__.py' adding 'statsmodels/datasets/scotland/data.py' adding 'statsmodels/datasets/scotland/scotvote.csv' adding 'statsmodels/datasets/spector/__init__.py' adding 'statsmodels/datasets/spector/data.py' adding 'statsmodels/datasets/spector/spector.csv' adding 'statsmodels/datasets/stackloss/__init__.py' adding 'statsmodels/datasets/stackloss/data.py' adding 'statsmodels/datasets/stackloss/stackloss.csv' adding 'statsmodels/datasets/star98/__init__.py' adding 'statsmodels/datasets/star98/data.py' adding 'statsmodels/datasets/star98/star98.csv' adding 'statsmodels/datasets/statecrime/__init__.py' adding 'statsmodels/datasets/statecrime/data.py' adding 'statsmodels/datasets/statecrime/statecrime.csv' adding 'statsmodels/datasets/strikes/__init__.py' adding 'statsmodels/datasets/strikes/data.py' adding 'statsmodels/datasets/strikes/strikes.csv' adding 'statsmodels/datasets/sunspots/__init__.py' adding 'statsmodels/datasets/sunspots/data.py' adding 'statsmodels/datasets/sunspots/sunspots.csv' adding 'statsmodels/datasets/tests/__init__.py' adding 'statsmodels/datasets/tests/test_data.py' adding 'statsmodels/datasets/tests/test_utils.py' adding 'statsmodels/discrete/__init__.py' adding 'statsmodels/discrete/_diagnostics_count.py' adding 'statsmodels/discrete/conditional_models.py' adding 'statsmodels/discrete/count_model.py' adding 'statsmodels/discrete/diagnostic.py' adding 'statsmodels/discrete/discrete_margins.py' adding 'statsmodels/discrete/discrete_model.py' adding 'statsmodels/discrete/truncated_model.py' adding 'statsmodels/discrete/tests/__init__.py' adding 'statsmodels/discrete/tests/test_conditional.py' adding 'statsmodels/discrete/tests/test_constrained.py' adding 'statsmodels/discrete/tests/test_count_model.py' adding 'statsmodels/discrete/tests/test_diagnostic.py' adding 'statsmodels/discrete/tests/test_discrete.py' adding 'statsmodels/discrete/tests/test_margins.py' adding 'statsmodels/discrete/tests/test_predict.py' adding 'statsmodels/discrete/tests/test_sandwich_cov.py' adding 'statsmodels/discrete/tests/test_truncated_model.py' adding 'statsmodels/discrete/tests/results/__init__.py' adding 'statsmodels/discrete/tests/results/mn_logit_summary.txt' adding 'statsmodels/discrete/tests/results/mnlogit_resid.csv' adding 'statsmodels/discrete/tests/results/nbinom_resids.csv' adding 'statsmodels/discrete/tests/results/phat_mnlogit.csv' adding 'statsmodels/discrete/tests/results/poisson_resid.csv' adding 'statsmodels/discrete/tests/results/predict_prob_poisson.csv' adding 'statsmodels/discrete/tests/results/results_count_margins.py' adding 'statsmodels/discrete/tests/results/results_count_robust_cluster.py' adding 'statsmodels/discrete/tests/results/results_discrete.py' adding 'statsmodels/discrete/tests/results/results_glm_logit_constrained.py' adding 'statsmodels/discrete/tests/results/results_poisson_constrained.py' adding 'statsmodels/discrete/tests/results/results_predict.py' adding 'statsmodels/discrete/tests/results/results_truncated.py' adding 'statsmodels/discrete/tests/results/results_truncated_st.py' adding 'statsmodels/discrete/tests/results/ships.csv' adding 'statsmodels/discrete/tests/results/sm3533.csv' adding 'statsmodels/discrete/tests/results/yhat_mnlogit.csv' adding 'statsmodels/discrete/tests/results/yhat_poisson.csv' adding 'statsmodels/distributions/__init__.py' adding 'statsmodels/distributions/bernstein.py' adding 'statsmodels/distributions/discrete.py' adding 'statsmodels/distributions/edgeworth.py' adding 'statsmodels/distributions/empirical_distribution.py' adding 'statsmodels/distributions/mixture_rvs.py' adding 'statsmodels/distributions/tools.py' adding 'statsmodels/distributions/copula/__init__.py' adding 'statsmodels/distributions/copula/_special.py' adding 'statsmodels/distributions/copula/api.py' adding 'statsmodels/distributions/copula/archimedean.py' adding 'statsmodels/distributions/copula/copulas.py' adding 'statsmodels/distributions/copula/depfunc_ev.py' adding 'statsmodels/distributions/copula/elliptical.py' adding 'statsmodels/distributions/copula/extreme_value.py' adding 'statsmodels/distributions/copula/other_copulas.py' adding 'statsmodels/distributions/copula/transforms.py' adding 'statsmodels/distributions/tests/__init__.py' adding 'statsmodels/distributions/tests/test_bernstein.py' adding 'statsmodels/distributions/tests/test_discrete.py' adding 'statsmodels/distributions/tests/test_ecdf.py' adding 'statsmodels/distributions/tests/test_edgeworth.py' adding 'statsmodels/distributions/tests/test_mixture.py' adding 'statsmodels/distributions/tests/test_tools.py' adding 'statsmodels/duration/__init__.py' adding 'statsmodels/duration/_kernel_estimates.py' adding 'statsmodels/duration/api.py' adding 'statsmodels/duration/hazard_regression.py' adding 'statsmodels/duration/survfunc.py' adding 'statsmodels/duration/tests/__init__.py' adding 'statsmodels/duration/tests/test_phreg.py' adding 'statsmodels/duration/tests/test_survfunc.py' adding 'statsmodels/duration/tests/results/__init__.py' adding 'statsmodels/duration/tests/results/bmt.csv' adding 'statsmodels/duration/tests/results/bmt_results.csv' adding 'statsmodels/duration/tests/results/phreg_gentests.py' adding 'statsmodels/duration/tests/results/survival_data_1000_10.csv' adding 'statsmodels/duration/tests/results/survival_data_100_5.csv' adding 'statsmodels/duration/tests/results/survival_data_20_1.csv' adding 'statsmodels/duration/tests/results/survival_data_50_1.csv' adding 'statsmodels/duration/tests/results/survival_data_50_2.csv' adding 'statsmodels/duration/tests/results/survival_enet_r_results.py' adding 'statsmodels/duration/tests/results/survival_r_results.py' adding 'statsmodels/emplike/__init__.py' adding 'statsmodels/emplike/aft_el.py' adding 'statsmodels/emplike/api.py' adding 'statsmodels/emplike/descriptive.py' adding 'statsmodels/emplike/elanova.py' adding 'statsmodels/emplike/elregress.py' adding 'statsmodels/emplike/originregress.py' adding 'statsmodels/emplike/tests/__init__.py' adding 'statsmodels/emplike/tests/test_aft.py' adding 'statsmodels/emplike/tests/test_anova.py' adding 'statsmodels/emplike/tests/test_descriptive.py' adding 'statsmodels/emplike/tests/test_origin.py' adding 'statsmodels/emplike/tests/test_regression.py' adding 'statsmodels/emplike/tests/results/__init__.py' adding 'statsmodels/emplike/tests/results/el_results.py' adding 'statsmodels/formula/__init__.py' adding 'statsmodels/formula/api.py' adding 'statsmodels/formula/formulatools.py' adding 'statsmodels/formula/tests/__init__.py' adding 'statsmodels/formula/tests/test_formula.py' adding 'statsmodels/gam/__init__.py' adding 'statsmodels/gam/api.py' adding 'statsmodels/gam/gam_penalties.py' adding 'statsmodels/gam/generalized_additive_model.py' adding 'statsmodels/gam/smooth_basis.py' adding 'statsmodels/gam/gam_cross_validation/__init__.py' adding 'statsmodels/gam/gam_cross_validation/cross_validators.py' adding 'statsmodels/gam/gam_cross_validation/gam_cross_validation.py' adding 'statsmodels/gam/tests/__init__.py' adding 'statsmodels/gam/tests/test_gam.py' adding 'statsmodels/gam/tests/test_penalized.py' adding 'statsmodels/gam/tests/test_smooth_basis.py' adding 'statsmodels/gam/tests/results/__init__.py' adding 'statsmodels/gam/tests/results/autos.csv' adding 'statsmodels/gam/tests/results/autos_exog.csv' adding 'statsmodels/gam/tests/results/autos_predict.csv' adding 'statsmodels/gam/tests/results/cubic_cyclic_splines_from_mgcv.csv' adding 'statsmodels/gam/tests/results/gam_PIRLS_results.csv' adding 'statsmodels/gam/tests/results/logit_gam_mgcv.csv' adding 'statsmodels/gam/tests/results/motorcycle.csv' adding 'statsmodels/gam/tests/results/prediction_from_mgcv.csv' adding 'statsmodels/gam/tests/results/results_mpg_bs.py' adding 'statsmodels/gam/tests/results/results_mpg_bs_poisson.py' adding 'statsmodels/gam/tests/results/results_pls.py' adding 'statsmodels/genmod/__init__.py' adding 'statsmodels/genmod/_tweedie_compound_poisson.py' adding 'statsmodels/genmod/api.py' adding 'statsmodels/genmod/bayes_mixed_glm.py' adding 'statsmodels/genmod/cov_struct.py' adding 'statsmodels/genmod/generalized_estimating_equations.py' adding 'statsmodels/genmod/generalized_linear_model.py' adding 'statsmodels/genmod/qif.py' adding 'statsmodels/genmod/families/__init__.py' adding 'statsmodels/genmod/families/family.py' adding 'statsmodels/genmod/families/links.py' adding 'statsmodels/genmod/families/varfuncs.py' adding 'statsmodels/genmod/families/tests/__init__.py' adding 'statsmodels/genmod/families/tests/test_family.py' adding 'statsmodels/genmod/families/tests/test_link.py' adding 'statsmodels/genmod/tests/__init__.py' adding 'statsmodels/genmod/tests/gee_categorical_simulation_check.py' adding 'statsmodels/genmod/tests/gee_gaussian_simulation_check.py' adding 'statsmodels/genmod/tests/gee_poisson_simulation_check.py' adding 'statsmodels/genmod/tests/gee_simulation_check.py' adding 'statsmodels/genmod/tests/test_bayes_mixed_glm.py' adding 'statsmodels/genmod/tests/test_constrained.py' adding 'statsmodels/genmod/tests/test_gee.py' adding 'statsmodels/genmod/tests/test_gee_glm.py' adding 'statsmodels/genmod/tests/test_glm.py' adding 'statsmodels/genmod/tests/test_glm_weights.py' adding 'statsmodels/genmod/tests/test_qif.py' adding 'statsmodels/genmod/tests/test_score_test.py' adding 'statsmodels/genmod/tests/results/__init__.py' adding 'statsmodels/genmod/tests/results/elastic_net_generate_tests.py' adding 'statsmodels/genmod/tests/results/enet_binomial.csv' adding 'statsmodels/genmod/tests/results/enet_poisson.csv' adding 'statsmodels/genmod/tests/results/epil.csv' adding 'statsmodels/genmod/tests/results/gee_generate_tests.py' adding 'statsmodels/genmod/tests/results/gee_linear_1.csv' adding 'statsmodels/genmod/tests/results/gee_logistic_1.csv' adding 'statsmodels/genmod/tests/results/gee_nested_linear_1.csv' adding 'statsmodels/genmod/tests/results/gee_nominal_1.csv' adding 'statsmodels/genmod/tests/results/gee_ordinal_1.csv' adding 'statsmodels/genmod/tests/results/gee_poisson_1.csv' adding 'statsmodels/genmod/tests/results/glm_test_resids.py' adding 'statsmodels/genmod/tests/results/glmnet_r_results.py' adding 'statsmodels/genmod/tests/results/igaussident_resids.csv' adding 'statsmodels/genmod/tests/results/inv_gaussian.csv' adding 'statsmodels/genmod/tests/results/iris.csv' adding 'statsmodels/genmod/tests/results/medparlogresids.csv' adding 'statsmodels/genmod/tests/results/res_R_var_weight.py' adding 'statsmodels/genmod/tests/results/results_glm.py' adding 'statsmodels/genmod/tests/results/results_glm_poisson_weights.py' adding 'statsmodels/genmod/tests/results/results_tweedie_aweights_nonrobust.csv' adding 'statsmodels/genmod/tests/results/stata_cancer_glm.csv' adding 'statsmodels/genmod/tests/results/stata_lbw_glm.csv' adding 'statsmodels/genmod/tests/results/stata_medpar1_glm.csv' adding 'statsmodels/graphics/__init__.py' adding 'statsmodels/graphics/_regressionplots_doc.py' adding 'statsmodels/graphics/agreement.py' adding 'statsmodels/graphics/api.py' adding 'statsmodels/graphics/boxplots.py' adding 'statsmodels/graphics/correlation.py' adding 'statsmodels/graphics/dotplots.py' adding 'statsmodels/graphics/factorplots.py' adding 'statsmodels/graphics/functional.py' adding 'statsmodels/graphics/gofplots.py' adding 'statsmodels/graphics/mosaicplot.py' adding 'statsmodels/graphics/plot_grids.py' adding 'statsmodels/graphics/plottools.py' adding 'statsmodels/graphics/regressionplots.py' adding 'statsmodels/graphics/tsaplots.py' adding 'statsmodels/graphics/tukeyplot.py' adding 'statsmodels/graphics/utils.py' adding 'statsmodels/graphics/tests/__init__.py' adding 'statsmodels/graphics/tests/test_agreement.py' adding 'statsmodels/graphics/tests/test_boxplots.py' adding 'statsmodels/graphics/tests/test_correlation.py' adding 'statsmodels/graphics/tests/test_dotplot.py' adding 'statsmodels/graphics/tests/test_factorplots.py' adding 'statsmodels/graphics/tests/test_functional.py' adding 'statsmodels/graphics/tests/test_gofplots.py' adding 'statsmodels/graphics/tests/test_mosaicplot.py' adding 'statsmodels/graphics/tests/test_regressionplots.py' adding 'statsmodels/graphics/tests/test_tsaplots.py' adding 'statsmodels/imputation/__init__.py' adding 'statsmodels/imputation/bayes_mi.py' adding 'statsmodels/imputation/mice.py' adding 'statsmodels/imputation/ros.py' adding 'statsmodels/imputation/tests/__init__.py' adding 'statsmodels/imputation/tests/test_bayes_mi.py' adding 'statsmodels/imputation/tests/test_mice.py' adding 'statsmodels/imputation/tests/test_ros.py' adding 'statsmodels/interface/__init__.py' adding 'statsmodels/iolib/__init__.py' adding 'statsmodels/iolib/api.py' adding 'statsmodels/iolib/foreign.py' adding 'statsmodels/iolib/openfile.py' adding 'statsmodels/iolib/smpickle.py' adding 'statsmodels/iolib/stata_summary_examples.py' adding 'statsmodels/iolib/summary.py' adding 'statsmodels/iolib/summary2.py' adding 'statsmodels/iolib/table.py' adding 'statsmodels/iolib/tableformatting.py' adding 'statsmodels/iolib/tests/__init__.py' adding 'statsmodels/iolib/tests/test_pickle.py' adding 'statsmodels/iolib/tests/test_summary.py' adding 'statsmodels/iolib/tests/test_summary2.py' adding 'statsmodels/iolib/tests/test_summary_old.py' adding 'statsmodels/iolib/tests/test_table.py' adding 'statsmodels/iolib/tests/test_table_econpy.py' adding 'statsmodels/iolib/tests/results/__init__.py' adding 'statsmodels/iolib/tests/results/data_missing.dta' adding 'statsmodels/iolib/tests/results/macrodata.py' adding 'statsmodels/iolib/tests/results/time_series_examples.dta' adding 'statsmodels/miscmodels/__init__.py' adding 'statsmodels/miscmodels/api.py' adding 'statsmodels/miscmodels/count.py' adding 'statsmodels/miscmodels/nonlinls.py' adding 'statsmodels/miscmodels/ordinal_model.py' adding 'statsmodels/miscmodels/tmodel.py' adding 'statsmodels/miscmodels/try_mlecov.py' adding 'statsmodels/miscmodels/tests/__init__.py' adding 'statsmodels/miscmodels/tests/results_tmodel.py' adding 'statsmodels/miscmodels/tests/test_generic_mle.py' adding 'statsmodels/miscmodels/tests/test_ordinal_model.py' adding 'statsmodels/miscmodels/tests/test_poisson.py' adding 'statsmodels/miscmodels/tests/test_tmodel.py' adding 'statsmodels/miscmodels/tests/results/__init__.py' adding 'statsmodels/miscmodels/tests/results/ologit_ucla.csv' adding 'statsmodels/miscmodels/tests/results/results_ordinal_model.py' adding 'statsmodels/multivariate/__init__.py' adding 'statsmodels/multivariate/api.py' adding 'statsmodels/multivariate/cancorr.py' adding 'statsmodels/multivariate/factor.py' adding 'statsmodels/multivariate/manova.py' adding 'statsmodels/multivariate/multivariate_ols.py' adding 'statsmodels/multivariate/pca.py' adding 'statsmodels/multivariate/plots.py' adding 'statsmodels/multivariate/factor_rotation/__init__.py' adding 'statsmodels/multivariate/factor_rotation/_analytic_rotation.py' adding 'statsmodels/multivariate/factor_rotation/_gpa_rotation.py' adding 'statsmodels/multivariate/factor_rotation/_wrappers.py' adding 'statsmodels/multivariate/factor_rotation/tests/__init__.py' adding 'statsmodels/multivariate/factor_rotation/tests/test_rotation.py' adding 'statsmodels/multivariate/tests/__init__.py' adding 'statsmodels/multivariate/tests/test_cancorr.py' adding 'statsmodels/multivariate/tests/test_factor.py' adding 'statsmodels/multivariate/tests/test_manova.py' adding 'statsmodels/multivariate/tests/test_ml_factor.py' adding 'statsmodels/multivariate/tests/test_multivariate_ols.py' adding 'statsmodels/multivariate/tests/test_pca.py' adding 'statsmodels/multivariate/tests/results/__init__.py' adding 'statsmodels/multivariate/tests/results/datamlw.py' adding 'statsmodels/multivariate/tests/results/factor_data.csv' adding 'statsmodels/multivariate/tests/results/factors_stata.csv' adding 'statsmodels/nonparametric/__init__.py' adding 'statsmodels/nonparametric/_kernel_base.py' adding 'statsmodels/nonparametric/_smoothers_lowess.cpython-314-riscv64-linux-gnu.so' adding 'statsmodels/nonparametric/api.py' adding 'statsmodels/nonparametric/bandwidths.py' adding 'statsmodels/nonparametric/kde.py' adding 'statsmodels/nonparametric/kdetools.py' adding 'statsmodels/nonparametric/kernel_density.py' adding 'statsmodels/nonparametric/kernel_regression.py' adding 'statsmodels/nonparametric/kernels.py' adding 'statsmodels/nonparametric/kernels_asymmetric.py' adding 'statsmodels/nonparametric/linbin.cpython-314-riscv64-linux-gnu.so' adding 'statsmodels/nonparametric/smoothers_lowess.py' adding 'statsmodels/nonparametric/smoothers_lowess_old.py' adding 'statsmodels/nonparametric/tests/__init__.py' adding 'statsmodels/nonparametric/tests/test_asymmetric.py' adding 'statsmodels/nonparametric/tests/test_bandwidths.py' adding 'statsmodels/nonparametric/tests/test_kde.py' adding 'statsmodels/nonparametric/tests/test_kernel_density.py' adding 'statsmodels/nonparametric/tests/test_kernel_regression.py' adding 'statsmodels/nonparametric/tests/test_kernels.py' adding 'statsmodels/nonparametric/tests/test_lowess.py' adding 'statsmodels/nonparametric/tests/results/__init__.py' adding 'statsmodels/nonparametric/tests/results/results_kcde.csv' adding 'statsmodels/nonparametric/tests/results/results_kde.csv' adding 'statsmodels/nonparametric/tests/results/results_kde_fft.csv' adding 'statsmodels/nonparametric/tests/results/results_kde_univ_weights.csv' adding 'statsmodels/nonparametric/tests/results/results_kde_weights.csv' adding 'statsmodels/nonparametric/tests/results/results_kernel_regression.csv' adding 'statsmodels/nonparametric/tests/results/test_lowess_delta.csv' adding 'statsmodels/nonparametric/tests/results/test_lowess_frac.csv' adding 'statsmodels/nonparametric/tests/results/test_lowess_iter.csv' adding 'statsmodels/nonparametric/tests/results/test_lowess_simple.csv' adding 'statsmodels/othermod/__init__.py' adding 'statsmodels/othermod/api.py' adding 'statsmodels/othermod/betareg.py' adding 'statsmodels/othermod/tests/__init__.py' adding 'statsmodels/othermod/tests/test_beta.py' adding 'statsmodels/othermod/tests/results/__init__.py' adding 'statsmodels/othermod/tests/results/foodexpenditure.csv' adding 'statsmodels/othermod/tests/results/methylation-test.csv' adding 'statsmodels/othermod/tests/results/resid_methylation.csv' adding 'statsmodels/othermod/tests/results/results_betareg.py' adding 'statsmodels/regression/__init__.py' adding 'statsmodels/regression/_prediction.py' adding 'statsmodels/regression/_tools.py' adding 'statsmodels/regression/dimred.py' adding 'statsmodels/regression/feasible_gls.py' adding 'statsmodels/regression/linear_model.py' adding 'statsmodels/regression/mixed_linear_model.py' adding 'statsmodels/regression/process_regression.py' adding 'statsmodels/regression/quantile_regression.py' adding 'statsmodels/regression/recursive_ls.py' adding 'statsmodels/regression/rolling.py' adding 'statsmodels/regression/tests/__init__.py' adding 'statsmodels/regression/tests/test_cov.py' adding 'statsmodels/regression/tests/test_dimred.py' adding 'statsmodels/regression/tests/test_glsar_gretl.py' adding 'statsmodels/regression/tests/test_glsar_stata.py' adding 'statsmodels/regression/tests/test_lme.py' adding 'statsmodels/regression/tests/test_predict.py' adding 'statsmodels/regression/tests/test_processreg.py' adding 'statsmodels/regression/tests/test_quantile_regression.py' adding 'statsmodels/regression/tests/test_recursive_ls.py' adding 'statsmodels/regression/tests/test_regression.py' adding 'statsmodels/regression/tests/test_robustcov.py' adding 'statsmodels/regression/tests/test_rolling.py' adding 'statsmodels/regression/tests/test_theil.py' adding 'statsmodels/regression/tests/test_tools.py' adding 'statsmodels/regression/tests/results/__init__.py' adding 'statsmodels/regression/tests/results/dietox.csv' adding 'statsmodels/regression/tests/results/generate_lasso.py' adding 'statsmodels/regression/tests/results/generate_lme.py' adding 'statsmodels/regression/tests/results/glmnet_r_results.py' adding 'statsmodels/regression/tests/results/lasso_data.csv' adding 'statsmodels/regression/tests/results/leverage_influence_ols_nostars.txt' adding 'statsmodels/regression/tests/results/lme00.csv' adding 'statsmodels/regression/tests/results/lme01.csv' adding 'statsmodels/regression/tests/results/lme02.csv' adding 'statsmodels/regression/tests/results/lme03.csv' adding 'statsmodels/regression/tests/results/lme04.csv' adding 'statsmodels/regression/tests/results/lme05.csv' adding 'statsmodels/regression/tests/results/lme06.csv' adding 'statsmodels/regression/tests/results/lme07.csv' adding 'statsmodels/regression/tests/results/lme08.csv' adding 'statsmodels/regression/tests/results/lme09.csv' adding 'statsmodels/regression/tests/results/lme10.csv' adding 'statsmodels/regression/tests/results/lme11.csv' adding 'statsmodels/regression/tests/results/lme_r_results.py' adding 'statsmodels/regression/tests/results/macro_gr_corc_stata.py' adding 'statsmodels/regression/tests/results/pastes.csv' adding 'statsmodels/regression/tests/results/results_grunfeld_ols_robust_cluster.py' adding 'statsmodels/regression/tests/results/results_macro_ols_robust.py' adding 'statsmodels/regression/tests/results/results_quantile_regression.py' adding 'statsmodels/regression/tests/results/results_regression.py' adding 'statsmodels/regression/tests/results/results_rls_R.csv' adding 'statsmodels/regression/tests/results/results_rls_stata.csv' adding 'statsmodels/regression/tests/results/results_theil_textile.py' adding 'statsmodels/regression/tests/results/theil_textile_predict.csv' adding 'statsmodels/robust/__init__.py' adding 'statsmodels/robust/_qn.cpython-314-riscv64-linux-gnu.so' adding 'statsmodels/robust/norms.py' adding 'statsmodels/robust/robust_linear_model.py' adding 'statsmodels/robust/scale.py' adding 'statsmodels/robust/tests/__init__.py' adding 'statsmodels/robust/tests/test_mquantiles.py' adding 'statsmodels/robust/tests/test_norms.py' adding 'statsmodels/robust/tests/test_rlm.py' adding 'statsmodels/robust/tests/test_scale.py' adding 'statsmodels/robust/tests/results/__init__.py' adding 'statsmodels/robust/tests/results/results_norms.py' adding 'statsmodels/robust/tests/results/results_rlm.py' adding 'statsmodels/sandbox/__init__.py' adding 'statsmodels/sandbox/bspline.py' adding 'statsmodels/sandbox/descstats.py' adding 'statsmodels/sandbox/gam.py' adding 'statsmodels/sandbox/infotheo.py' adding 'statsmodels/sandbox/mle.py' adding 'statsmodels/sandbox/multilinear.py' adding 'statsmodels/sandbox/pca.py' adding 'statsmodels/sandbox/predict_functional.py' adding 'statsmodels/sandbox/rls.py' adding 'statsmodels/sandbox/sysreg.py' adding 'statsmodels/sandbox/archive/__init__.py' adding 'statsmodels/sandbox/archive/linalg_covmat.py' adding 'statsmodels/sandbox/archive/linalg_decomp_1.py' adding 'statsmodels/sandbox/archive/tsa.py' adding 'statsmodels/sandbox/datarich/__init__.py' adding 'statsmodels/sandbox/datarich/factormodels.py' adding 'statsmodels/sandbox/distributions/__init__.py' adding 'statsmodels/sandbox/distributions/estimators.py' adding 'statsmodels/sandbox/distributions/extras.py' adding 'statsmodels/sandbox/distributions/genpareto.py' adding 'statsmodels/sandbox/distributions/gof_new.py' adding 'statsmodels/sandbox/distributions/multivariate.py' adding 'statsmodels/sandbox/distributions/mv_measures.py' adding 'statsmodels/sandbox/distributions/mv_normal.py' adding 'statsmodels/sandbox/distributions/otherdist.py' adding 'statsmodels/sandbox/distributions/quantize.py' adding 'statsmodels/sandbox/distributions/sppatch.py' adding 'statsmodels/sandbox/distributions/transform_functions.py' adding 'statsmodels/sandbox/distributions/transformed.py' adding 'statsmodels/sandbox/distributions/try_max.py' adding 'statsmodels/sandbox/distributions/try_pot.py' adding 'statsmodels/sandbox/distributions/examples/__init__.py' adding 'statsmodels/sandbox/distributions/examples/ex_extras.py' adding 'statsmodels/sandbox/distributions/examples/ex_fitfr.py' adding 'statsmodels/sandbox/distributions/examples/ex_gof.py' adding 'statsmodels/sandbox/distributions/examples/ex_mvelliptical.py' adding 'statsmodels/sandbox/distributions/examples/ex_transf2.py' adding 'statsmodels/sandbox/distributions/examples/matchdist.py' adding 'statsmodels/sandbox/distributions/tests/__init__.py' adding 'statsmodels/sandbox/distributions/tests/_est_fit.py' adding 'statsmodels/sandbox/distributions/tests/check_moments.py' adding 'statsmodels/sandbox/distributions/tests/distparams.py' adding 'statsmodels/sandbox/distributions/tests/test_extras.py' adding 'statsmodels/sandbox/distributions/tests/test_gof_new.py' adding 'statsmodels/sandbox/distributions/tests/test_multivariate.py' adding 'statsmodels/sandbox/distributions/tests/test_norm_expan.py' adding 'statsmodels/sandbox/distributions/tests/test_transf.py' adding 'statsmodels/sandbox/mcevaluate/__init__.py' adding 'statsmodels/sandbox/mcevaluate/arma.py' adding 'statsmodels/sandbox/nonparametric/__init__.py' adding 'statsmodels/sandbox/nonparametric/densityorthopoly.py' adding 'statsmodels/sandbox/nonparametric/dgp_examples.py' adding 'statsmodels/sandbox/nonparametric/kde2.py' adding 'statsmodels/sandbox/nonparametric/kdecovclass.py' adding 'statsmodels/sandbox/nonparametric/kernel_extras.py' adding 'statsmodels/sandbox/nonparametric/kernels.py' adding 'statsmodels/sandbox/nonparametric/smoothers.py' adding 'statsmodels/sandbox/nonparametric/testdata.py' adding 'statsmodels/sandbox/nonparametric/tests/__init__.py' adding 'statsmodels/sandbox/nonparametric/tests/ex_gam_am_new.py' adding 'statsmodels/sandbox/nonparametric/tests/ex_gam_new.py' adding 'statsmodels/sandbox/nonparametric/tests/ex_smoothers.py' adding 'statsmodels/sandbox/nonparametric/tests/test_kernel_extras.py' adding 'statsmodels/sandbox/nonparametric/tests/test_smoothers.py' adding 'statsmodels/sandbox/panel/__init__.py' adding 'statsmodels/sandbox/panel/correlation_structures.py' adding 'statsmodels/sandbox/panel/mixed.py' adding 'statsmodels/sandbox/panel/panel_short.py' adding 'statsmodels/sandbox/panel/panelmod.py' adding 'statsmodels/sandbox/panel/random_panel.py' adding 'statsmodels/sandbox/panel/sandwich_covariance_generic.py' adding 'statsmodels/sandbox/panel/tests/__init__.py' adding 'statsmodels/sandbox/panel/tests/test_random_panel.py' adding 'statsmodels/sandbox/regression/__init__.py' adding 'statsmodels/sandbox/regression/anova_nistcertified.py' adding 'statsmodels/sandbox/regression/ar_panel.py' adding 'statsmodels/sandbox/regression/example_kernridge.py' adding 'statsmodels/sandbox/regression/gmm.py' adding 'statsmodels/sandbox/regression/kernridgeregress_class.py' adding 'statsmodels/sandbox/regression/ols_anova_original.py' adding 'statsmodels/sandbox/regression/onewaygls.py' adding 'statsmodels/sandbox/regression/penalized.py' adding 'statsmodels/sandbox/regression/predstd.py' adding 'statsmodels/sandbox/regression/runmnl.py' adding 'statsmodels/sandbox/regression/sympy_diff.py' adding 'statsmodels/sandbox/regression/tools.py' adding 'statsmodels/sandbox/regression/treewalkerclass.py' adding 'statsmodels/sandbox/regression/try_catdata.py' adding 'statsmodels/sandbox/regression/try_ols_anova.py' adding 'statsmodels/sandbox/regression/try_treewalker.py' adding 'statsmodels/sandbox/regression/tests/__init__.py' adding 'statsmodels/sandbox/regression/tests/griliches76.dta' adding 'statsmodels/sandbox/regression/tests/racd10data_with_transformed.csv' adding 'statsmodels/sandbox/regression/tests/results_gmm_griliches.py' adding 'statsmodels/sandbox/regression/tests/results_gmm_griliches_iter.py' adding 'statsmodels/sandbox/regression/tests/results_gmm_poisson.py' adding 'statsmodels/sandbox/regression/tests/results_ivreg2_griliches.py' adding 'statsmodels/sandbox/regression/tests/test_gmm.py' adding 'statsmodels/sandbox/regression/tests/test_gmm_poisson.py' adding 'statsmodels/sandbox/stats/__init__.py' adding 'statsmodels/sandbox/stats/contrast_tools.py' adding 'statsmodels/sandbox/stats/diagnostic.py' adding 'statsmodels/sandbox/stats/ex_newtests.py' adding 'statsmodels/sandbox/stats/multicomp.py' adding 'statsmodels/sandbox/stats/runs.py' adding 'statsmodels/sandbox/stats/stats_dhuard.py' adding 'statsmodels/sandbox/stats/stats_mstats_short.py' adding 'statsmodels/sandbox/stats/tests/__init__.py' adding 'statsmodels/sandbox/stats/tests/test_multicomp.py' adding 'statsmodels/sandbox/stats/tests/test_runs.py' adding 'statsmodels/sandbox/tests/__init__.py' adding 'statsmodels/sandbox/tests/maketests_mlabwrap.py' adding 'statsmodels/sandbox/tests/savervs.py' adding 'statsmodels/sandbox/tests/test_gam.py' adding 'statsmodels/sandbox/tests/test_pca.py' adding 'statsmodels/sandbox/tests/test_predict_functional.py' adding 'statsmodels/sandbox/tools/__init__.py' adding 'statsmodels/sandbox/tools/cross_val.py' adding 'statsmodels/sandbox/tools/mctools.py' adding 'statsmodels/sandbox/tools/tools_pca.py' adding 'statsmodels/sandbox/tools/try_mctools.py' adding 'statsmodels/sandbox/tsa/__init__.py' adding 'statsmodels/sandbox/tsa/diffusion.py' adding 'statsmodels/sandbox/tsa/diffusion2.py' adding 'statsmodels/sandbox/tsa/example_arma.py' adding 'statsmodels/sandbox/tsa/fftarma.py' adding 'statsmodels/sandbox/tsa/movstat.py' adding 'statsmodels/sandbox/tsa/try_arma_more.py' adding 'statsmodels/sandbox/tsa/try_fi.py' adding 'statsmodels/sandbox/tsa/try_var_convolve.py' adding 'statsmodels/sandbox/tsa/varma.py' adding 'statsmodels/src/__init__.py' adding 'statsmodels/stats/__init__.py' adding 'statsmodels/stats/_adnorm.py' adding 'statsmodels/stats/_delta_method.py' adding 'statsmodels/stats/_diagnostic_other.py' adding 'statsmodels/stats/_inference_tools.py' adding 'statsmodels/stats/_knockoff.py' adding 'statsmodels/stats/_lilliefors.py' adding 'statsmodels/stats/_lilliefors_critical_values.py' adding 'statsmodels/stats/anova.py' adding 'statsmodels/stats/api.py' adding 'statsmodels/stats/base.py' adding 'statsmodels/stats/contingency_tables.py' adding 'statsmodels/stats/contrast.py' adding 'statsmodels/stats/correlation_tools.py' adding 'statsmodels/stats/descriptivestats.py' adding 'statsmodels/stats/diagnostic.py' adding 'statsmodels/stats/diagnostic_gen.py' adding 'statsmodels/stats/dist_dependence_measures.py' adding 'statsmodels/stats/effect_size.py' adding 'statsmodels/stats/gof.py' adding 'statsmodels/stats/inter_rater.py' adding 'statsmodels/stats/knockoff_regeffects.py' adding 'statsmodels/stats/mediation.py' adding 'statsmodels/stats/meta_analysis.py' adding 'statsmodels/stats/moment_helpers.py' adding 'statsmodels/stats/multicomp.py' adding 'statsmodels/stats/multitest.py' adding 'statsmodels/stats/multivariate.py' adding 'statsmodels/stats/multivariate_tools.py' adding 'statsmodels/stats/nonparametric.py' adding 'statsmodels/stats/oaxaca.py' adding 'statsmodels/stats/oneway.py' adding 'statsmodels/stats/outliers_influence.py' adding 'statsmodels/stats/power.py' adding 'statsmodels/stats/proportion.py' adding 'statsmodels/stats/rates.py' adding 'statsmodels/stats/regularized_covariance.py' adding 'statsmodels/stats/robust_compare.py' adding 'statsmodels/stats/sandwich_covariance.py' adding 'statsmodels/stats/stattools.py' adding 'statsmodels/stats/tabledist.py' adding 'statsmodels/stats/weightstats.py' adding 'statsmodels/stats/libqsturng/CH.r' adding 'statsmodels/stats/libqsturng/LICENSE.txt' adding 'statsmodels/stats/libqsturng/__init__.py' adding 'statsmodels/stats/libqsturng/make_tbls.py' adding 'statsmodels/stats/libqsturng/qsturng_.py' adding 'statsmodels/stats/libqsturng/tests/__init__.py' adding 'statsmodels/stats/libqsturng/tests/bootleg.dat' adding 'statsmodels/stats/libqsturng/tests/test_qsturng.py' adding 'statsmodels/stats/tests/__init__.py' adding 'statsmodels/stats/tests/test_anova.py' adding 'statsmodels/stats/tests/test_anova_rm.py' adding 'statsmodels/stats/tests/test_base.py' adding 'statsmodels/stats/tests/test_contingency_tables.py' adding 'statsmodels/stats/tests/test_contrast.py' adding 'statsmodels/stats/tests/test_correlation.py' adding 'statsmodels/stats/tests/test_corrpsd.py' adding 'statsmodels/stats/tests/test_data.txt' adding 'statsmodels/stats/tests/test_deltacov.py' adding 'statsmodels/stats/tests/test_descriptivestats.py' adding 'statsmodels/stats/tests/test_diagnostic.py' adding 'statsmodels/stats/tests/test_diagnostic_other.py' adding 'statsmodels/stats/tests/test_dist_dependant_measures.py' adding 'statsmodels/stats/tests/test_effectsize.py' adding 'statsmodels/stats/tests/test_gof.py' adding 'statsmodels/stats/tests/test_groups_sw.py' adding 'statsmodels/stats/tests/test_influence.py' adding 'statsmodels/stats/tests/test_inter_rater.py' adding 'statsmodels/stats/tests/test_knockoff.py' adding 'statsmodels/stats/tests/test_lilliefors.py' adding 'statsmodels/stats/tests/test_mediation.py' adding 'statsmodels/stats/tests/test_meta.py' adding 'statsmodels/stats/tests/test_moment_helpers.py' adding 'statsmodels/stats/tests/test_multi.py' adding 'statsmodels/stats/tests/test_multivariate.py' adding 'statsmodels/stats/tests/test_nonparametric.py' adding 'statsmodels/stats/tests/test_oaxaca.py' adding 'statsmodels/stats/tests/test_oneway.py' adding 'statsmodels/stats/tests/test_outliers_influence.py' adding 'statsmodels/stats/tests/test_pairwise.py' adding 'statsmodels/stats/tests/test_panel_robustcov.py' adding 'statsmodels/stats/tests/test_power.py' adding 'statsmodels/stats/tests/test_proportion.py' adding 'statsmodels/stats/tests/test_qsturng.py' adding 'statsmodels/stats/tests/test_rates_poisson.py' adding 'statsmodels/stats/tests/test_regularized_covariance.py' adding 'statsmodels/stats/tests/test_robust_compare.py' adding 'statsmodels/stats/tests/test_sandwich.py' adding 'statsmodels/stats/tests/test_statstools.py' adding 'statsmodels/stats/tests/test_tabledist.py' adding 'statsmodels/stats/tests/test_tost.py' adding 'statsmodels/stats/tests/test_weightstats.py' adding 'statsmodels/stats/tests/results/__init__.py' adding 'statsmodels/stats/tests/results/binary_constrict.csv' adding 'statsmodels/stats/tests/results/bootleg.csv' adding 'statsmodels/stats/tests/results/contingency_table_r_results.csv' adding 'statsmodels/stats/tests/results/data.dat' adding 'statsmodels/stats/tests/results/framing.csv' adding 'statsmodels/stats/tests/results/influence_lsdiag_R.json' adding 'statsmodels/stats/tests/results/influence_measures_R.csv' adding 'statsmodels/stats/tests/results/influence_measures_bool_R.csv' adding 'statsmodels/stats/tests/results/lilliefors_critical_value_simulation.py' adding 'statsmodels/stats/tests/results/results_influence_logit.csv' adding 'statsmodels/stats/tests/results/results_meta.py' adding 'statsmodels/stats/tests/results/results_multinomial_proportions.py' adding 'statsmodels/stats/tests/results/results_panelrobust.py' adding 'statsmodels/stats/tests/results/results_power.py' adding 'statsmodels/stats/tests/results/results_proportion.py' adding 'statsmodels/stats/tests/results/results_rates.py' adding 'statsmodels/stats/tests/results/wspec1.csv' adding 'statsmodels/stats/tests/results/wspec2.csv' adding 'statsmodels/stats/tests/results/wspec3.csv' adding 'statsmodels/stats/tests/results/wspec4.csv' adding 'statsmodels/tests/__init__.py' adding 'statsmodels/tests/test_package.py' adding 'statsmodels/tests/test_x13.py' adding 'statsmodels/tools/__init__.py' adding 'statsmodels/tools/_test_runner.py' adding 'statsmodels/tools/_testing.py' adding 'statsmodels/tools/catadd.py' adding 'statsmodels/tools/data.py' adding 'statsmodels/tools/decorators.py' adding 'statsmodels/tools/docstring.py' adding 'statsmodels/tools/eval_measures.py' adding 'statsmodels/tools/grouputils.py' adding 'statsmodels/tools/linalg.py' adding 'statsmodels/tools/numdiff.py' adding 'statsmodels/tools/parallel.py' adding 'statsmodels/tools/print_version.py' adding 'statsmodels/tools/rng_qrng.py' adding 'statsmodels/tools/rootfinding.py' adding 'statsmodels/tools/sequences.py' adding 'statsmodels/tools/sm_exceptions.py' adding 'statsmodels/tools/testing.py' adding 'statsmodels/tools/tools.py' adding 'statsmodels/tools/transform_model.py' adding 'statsmodels/tools/typing.py' adding 'statsmodels/tools/web.py' adding 'statsmodels/tools/tests/__init__.py' adding 'statsmodels/tools/tests/test_catadd.py' adding 'statsmodels/tools/tests/test_data.py' adding 'statsmodels/tools/tests/test_decorators.py' adding 'statsmodels/tools/tests/test_docstring.py' adding 'statsmodels/tools/tests/test_eval_measures.py' adding 'statsmodels/tools/tests/test_grouputils.py' adding 'statsmodels/tools/tests/test_linalg.py' adding 'statsmodels/tools/tests/test_numdiff.py' adding 'statsmodels/tools/tests/test_parallel.py' adding 'statsmodels/tools/tests/test_rootfinding.py' adding 'statsmodels/tools/tests/test_sequences.py' adding 'statsmodels/tools/tests/test_testing.py' adding 'statsmodels/tools/tests/test_tools.py' adding 'statsmodels/tools/tests/test_transform_model.py' adding 'statsmodels/tools/tests/test_web.py' adding 'statsmodels/tools/validation/__init__.py' adding 'statsmodels/tools/validation/decorators.py' adding 'statsmodels/tools/validation/validation.py' adding 'statsmodels/tools/validation/tests/__init__.py' adding 'statsmodels/tools/validation/tests/test_validation.py' adding 'statsmodels/treatment/__init__.py' adding 'statsmodels/treatment/treatment_effects.py' adding 'statsmodels/treatment/tests/__init__.py' adding 'statsmodels/treatment/tests/test_teffects.py' adding 'statsmodels/treatment/tests/results/__init__.py' adding 'statsmodels/treatment/tests/results/cataneo2.csv' adding 'statsmodels/treatment/tests/results/results_teffects.py' adding 'statsmodels/tsa/__init__.py' adding 'statsmodels/tsa/_bds.py' adding 'statsmodels/tsa/_innovations.cpython-314-riscv64-linux-gnu.so' adding 'statsmodels/tsa/adfvalues.py' adding 'statsmodels/tsa/api.py' adding 'statsmodels/tsa/ar_model.py' adding 'statsmodels/tsa/arima_model.py' adding 'statsmodels/tsa/arima_process.py' adding 'statsmodels/tsa/arma_mle.py' adding 'statsmodels/tsa/coint_tables.py' adding 'statsmodels/tsa/descriptivestats.py' adding 'statsmodels/tsa/deterministic.py' adding 'statsmodels/tsa/mlemodel.py' adding 'statsmodels/tsa/seasonal.py' adding 'statsmodels/tsa/stattools.py' adding 'statsmodels/tsa/tsatools.py' adding 'statsmodels/tsa/varma_process.py' adding 'statsmodels/tsa/x13.py' adding 'statsmodels/tsa/ardl/__init__.py' adding 'statsmodels/tsa/ardl/model.py' adding 'statsmodels/tsa/ardl/pss_critical_values.py' adding 'statsmodels/tsa/ardl/_pss_critical_values/__init__.py' adding 'statsmodels/tsa/ardl/_pss_critical_values/pss-process.py' adding 'statsmodels/tsa/ardl/_pss_critical_values/pss.py' adding 'statsmodels/tsa/ardl/tests/__init__.py' adding 'statsmodels/tsa/ardl/tests/test_ardl.py' adding 'statsmodels/tsa/arima/__init__.py' adding 'statsmodels/tsa/arima/api.py' adding 'statsmodels/tsa/arima/model.py' adding 'statsmodels/tsa/arima/params.py' adding 'statsmodels/tsa/arima/specification.py' adding 'statsmodels/tsa/arima/tools.py' adding 'statsmodels/tsa/arima/datasets/__init__.py' adding 'statsmodels/tsa/arima/datasets/brockwell_davis_2002/__init__.py' adding 'statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/__init__.py' adding 'statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/dowj.py' adding 'statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/lake.py' adding 'statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/oshorts.py' adding 'statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/sbl.py' adding 'statsmodels/tsa/arima/estimators/__init__.py' adding 'statsmodels/tsa/arima/estimators/burg.py' adding 'statsmodels/tsa/arima/estimators/durbin_levinson.py' adding 'statsmodels/tsa/arima/estimators/gls.py' adding 'statsmodels/tsa/arima/estimators/hannan_rissanen.py' adding 'statsmodels/tsa/arima/estimators/innovations.py' adding 'statsmodels/tsa/arima/estimators/statespace.py' adding 'statsmodels/tsa/arima/estimators/yule_walker.py' adding 'statsmodels/tsa/arima/estimators/tests/__init__.py' adding 'statsmodels/tsa/arima/estimators/tests/test_burg.py' adding 'statsmodels/tsa/arima/estimators/tests/test_durbin_levinson.py' adding 'statsmodels/tsa/arima/estimators/tests/test_gls.py' adding 'statsmodels/tsa/arima/estimators/tests/test_hannan_rissanen.py' adding 'statsmodels/tsa/arima/estimators/tests/test_innovations.py' adding 'statsmodels/tsa/arima/estimators/tests/test_statespace.py' adding 'statsmodels/tsa/arima/estimators/tests/test_yule_walker.py' adding 'statsmodels/tsa/arima/tests/__init__.py' adding 'statsmodels/tsa/arima/tests/test_model.py' adding 'statsmodels/tsa/arima/tests/test_params.py' adding 'statsmodels/tsa/arima/tests/test_specification.py' adding 'statsmodels/tsa/arima/tests/test_tools.py' adding 'statsmodels/tsa/base/__init__.py' adding 'statsmodels/tsa/base/datetools.py' adding 'statsmodels/tsa/base/prediction.py' adding 'statsmodels/tsa/base/tsa_model.py' adding 'statsmodels/tsa/base/tests/__init__.py' adding 'statsmodels/tsa/base/tests/test_base.py' adding 'statsmodels/tsa/base/tests/test_datetools.py' adding 'statsmodels/tsa/base/tests/test_prediction.py' adding 'statsmodels/tsa/base/tests/test_tsa_indexes.py' adding 'statsmodels/tsa/exponential_smoothing/__init__.py' adding 'statsmodels/tsa/exponential_smoothing/_ets_smooth.cpython-314-riscv64-linux-gnu.so' adding 'statsmodels/tsa/exponential_smoothing/base.py' adding 'statsmodels/tsa/exponential_smoothing/ets.py' adding 'statsmodels/tsa/exponential_smoothing/initialization.py' adding 'statsmodels/tsa/filters/__init__.py' adding 'statsmodels/tsa/filters/_utils.py' adding 'statsmodels/tsa/filters/api.py' adding 'statsmodels/tsa/filters/bk_filter.py' adding 'statsmodels/tsa/filters/cf_filter.py' adding 'statsmodels/tsa/filters/filtertools.py' adding 'statsmodels/tsa/filters/hp_filter.py' adding 'statsmodels/tsa/filters/tests/__init__.py' adding 'statsmodels/tsa/filters/tests/test_filters.py' adding 'statsmodels/tsa/filters/tests/results/__init__.py' adding 'statsmodels/tsa/filters/tests/results/filter_results.py' adding 'statsmodels/tsa/forecasting/__init__.py' adding 'statsmodels/tsa/forecasting/stl.py' adding 'statsmodels/tsa/forecasting/theta.py' adding 'statsmodels/tsa/forecasting/tests/__init__.py' adding 'statsmodels/tsa/forecasting/tests/test_stl.py' adding 'statsmodels/tsa/forecasting/tests/test_theta.py' adding 'statsmodels/tsa/holtwinters/__init__.py' adding 'statsmodels/tsa/holtwinters/_exponential_smoothers.cpython-314-riscv64-linux-gnu.so' adding 'statsmodels/tsa/holtwinters/_smoothers.py' adding 'statsmodels/tsa/holtwinters/model.py' adding 'statsmodels/tsa/holtwinters/results.py' adding 'statsmodels/tsa/holtwinters/tests/__init__.py' adding 'statsmodels/tsa/holtwinters/tests/test_holtwinters.py' adding 'statsmodels/tsa/holtwinters/tests/results/__init__.py' adding 'statsmodels/tsa/holtwinters/tests/results/housing-data.csv' adding 'statsmodels/tsa/innovations/__init__.py' adding 'statsmodels/tsa/innovations/_arma_innovations.cpython-314-riscv64-linux-gnu.so' adding 'statsmodels/tsa/innovations/api.py' adding 'statsmodels/tsa/innovations/arma_innovations.py' adding 'statsmodels/tsa/innovations/tests/__init__.py' adding 'statsmodels/tsa/innovations/tests/test_arma_innovations.py' adding 'statsmodels/tsa/innovations/tests/test_cython_arma_innovations_fast.py' adding 'statsmodels/tsa/interp/__init__.py' adding 'statsmodels/tsa/interp/denton.py' adding 'statsmodels/tsa/interp/tests/__init__.py' adding 'statsmodels/tsa/interp/tests/test_denton.py' adding 'statsmodels/tsa/regime_switching/__init__.py' adding 'statsmodels/tsa/regime_switching/_hamilton_filter.cpython-314-riscv64-linux-gnu.so' adding 'statsmodels/tsa/regime_switching/_kim_smoother.cpython-314-riscv64-linux-gnu.so' adding 'statsmodels/tsa/regime_switching/markov_autoregression.py' adding 'statsmodels/tsa/regime_switching/markov_regression.py' adding 'statsmodels/tsa/regime_switching/markov_switching.py' adding 'statsmodels/tsa/regime_switching/tests/__init__.py' adding 'statsmodels/tsa/regime_switching/tests/test_markov_autoregression.py' adding 'statsmodels/tsa/regime_switching/tests/test_markov_regression.py' adding 'statsmodels/tsa/regime_switching/tests/test_markov_switching.py' adding 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'statsmodels/tsa/tests/results/arima211_results.py' adding 'statsmodels/tsa/tests/results/arima211nc_css_results.py' adding 'statsmodels/tsa/tests/results/arima211nc_results.py' adding 'statsmodels/tsa/tests/results/arima212_forecast.csv' adding 'statsmodels/tsa/tests/results/bds_data.csv' adding 'statsmodels/tsa/tests/results/bds_results.csv' adding 'statsmodels/tsa/tests/results/datamlw_tls.py' adding 'statsmodels/tsa/tests/results/fit_ets_results.json' adding 'statsmodels/tsa/tests/results/fit_ets_results_nonseasonal.json' adding 'statsmodels/tsa/tests/results/fit_ets_results_seasonal.json' adding 'statsmodels/tsa/tests/results/gnpdef.csv' adding 'statsmodels/tsa/tests/results/lutkepohl2.dta' adding 'statsmodels/tsa/tests/results/make_arma.py' adding 'statsmodels/tsa/tests/results/rand10000.csv' adding 'statsmodels/tsa/tests/results/resids_css_c.csv' adding 'statsmodels/tsa/tests/results/resids_css_nc.csv' adding 'statsmodels/tsa/tests/results/resids_exact_c.csv' adding 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adding 'statsmodels/tsa/tests/results/results_corrgram.csv' adding 'statsmodels/tsa/tests/results/results_process.py' adding 'statsmodels/tsa/tests/results/rgnp.csv' adding 'statsmodels/tsa/tests/results/rgnpq.csv' adding 'statsmodels/tsa/tests/results/savedrvs.py' adding 'statsmodels/tsa/tests/results/stkprc.csv' adding 'statsmodels/tsa/tests/results/y_arma_data.csv' adding 'statsmodels/tsa/tests/results/yhat_css_c.csv' adding 'statsmodels/tsa/tests/results/yhat_css_nc.csv' adding 'statsmodels/tsa/tests/results/yhat_exact_c.csv' adding 'statsmodels/tsa/tests/results/yhat_exact_nc.csv' adding 'statsmodels/tsa/vector_ar/__init__.py' adding 'statsmodels/tsa/vector_ar/api.py' adding 'statsmodels/tsa/vector_ar/hypothesis_test_results.py' adding 'statsmodels/tsa/vector_ar/irf.py' adding 'statsmodels/tsa/vector_ar/output.py' adding 'statsmodels/tsa/vector_ar/plotting.py' adding 'statsmodels/tsa/vector_ar/svar_model.py' adding 'statsmodels/tsa/vector_ar/util.py' adding 'statsmodels/tsa/vector_ar/var_model.py' adding 'statsmodels/tsa/vector_ar/vecm.py' adding 'statsmodels/tsa/vector_ar/tests/__init__.py' adding 'statsmodels/tsa/vector_ar/tests/example_svar.py' adding 'statsmodels/tsa/vector_ar/tests/test_coint.py' adding 'statsmodels/tsa/vector_ar/tests/test_svar.py' adding 'statsmodels/tsa/vector_ar/tests/test_var.py' adding 'statsmodels/tsa/vector_ar/tests/test_var_jmulti.py' adding 'statsmodels/tsa/vector_ar/tests/test_vecm.py' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/__init__.py' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons_realgdp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realgdp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realgdp_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons_realgdp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realgdp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realgdp_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons_realgdp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realgdp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realgdp_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons_realgdp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realgdp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realgdp_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons_realgdp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realgdp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realgdp_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons_realgdp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realgdp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realgdp_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realinv.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_var_output.py' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_vecm_output.py' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_granger_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_granger_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_inst_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_inst_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_granger_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_granger_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_inst_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_inst_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_granger_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_granger_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_inst_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_inst_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_granger_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_granger_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_inst_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_inst_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_granger_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_granger_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_inst_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_inst_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_granger_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_granger_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_inst_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_inst_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_granger_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_granger_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_inst_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_inst_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_granger_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_granger_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_inst_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_inst_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_granger_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_granger_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_inst_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_inst_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_Sigmau.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_diag.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_fc5.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_granger_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_granger_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_inst_causality_dp_r.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_inst_causality_r_dp.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_ir.txt' adding 'statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_lagorder.txt' adding 'statsmodels/tsa/vector_ar/tests/Matlab_results/__init__.py' adding 'statsmodels/tsa/vector_ar/tests/Matlab_results/test_coint.csv' adding 'statsmodels/tsa/vector_ar/tests/results/__init__.py' adding 'statsmodels/tsa/vector_ar/tests/results/e1.dat' adding 'statsmodels/tsa/vector_ar/tests/results/e2.dat' adding 'statsmodels/tsa/vector_ar/tests/results/e3.dat' adding 'statsmodels/tsa/vector_ar/tests/results/e4.dat' adding 'statsmodels/tsa/vector_ar/tests/results/e5.dat' adding 'statsmodels/tsa/vector_ar/tests/results/e6.dat' adding 'statsmodels/tsa/vector_ar/tests/results/results_svar.py' adding 'statsmodels/tsa/vector_ar/tests/results/results_svar_st.py' adding 'statsmodels/tsa/vector_ar/tests/results/results_var.py' adding 'statsmodels/tsa/vector_ar/tests/results/results_var_data.py' adding 'statsmodels/tsa/vector_ar/tests/results/vars_results.npz' adding 'statsmodels-0.14.7.dev0+g40e6a84d2.d20260830.dist-info/licenses/LICENSE.txt' adding 'statsmodels-0.14.7.dev0+g40e6a84d2.d20260830.dist-info/METADATA' adding 'statsmodels-0.14.7.dev0+g40e6a84d2.d20260830.dist-info/WHEEL' adding 'statsmodels-0.14.7.dev0+g40e6a84d2.d20260830.dist-info/top_level.txt' adding 'statsmodels-0.14.7.dev0+g40e6a84d2.d20260830.dist-info/RECORD' removing build/bdist.linux-riscv64/wheel Successfully built statsmodels-0.14.7.dev0+g40e6a84d2.d20260830-cp314-cp314-linux_riscv64.whl ==> Starting check()... ============================= test session starts ============================== platform linux -- Python 3.14.7, pytest-9.0.3, pluggy-1.6.0 -- /usr/bin/python cachedir: .pytest_cache rootdir: /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels configfile: setup.cfg plugins: xdist-3.8.0 collecting ... collected 17988 items statsmodels/base/tests/test_data.py::TestArrays::test_orig PASSED [ 0%] statsmodels/base/tests/test_data.py::TestArrays::test_endogexog PASSED [ 0%] statsmodels/base/tests/test_data.py::TestArrays::test_attach PASSED [ 0%] statsmodels/base/tests/test_data.py::TestArrays::test_names PASSED [ 0%] statsmodels/base/tests/test_data.py::TestArrays::test_labels PASSED [ 0%] statsmodels/base/tests/test_data.py::TestArrays2dEndog::test_orig PASSED [ 0%] statsmodels/base/tests/test_data.py::TestArrays2dEndog::test_attach PASSED [ 0%] statsmodels/base/tests/test_data.py::TestArrays2dEndog::test_names PASSED [ 0%] statsmodels/base/tests/test_data.py::TestArrays2dEndog::test_labels PASSED [ 0%] statsmodels/base/tests/test_data.py::TestArrays2dEndog::test_endogexog 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statsmodels/discrete/tests/test_conditional.py::test_conditional_mnlogit_grad PASSED [ 2%] statsmodels/discrete/tests/test_conditional.py::test_conditional_mnlogit_2d PASSED [ 2%] statsmodels/discrete/tests/test_conditional.py::test_conditional_mnlogit_3d PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained1a::test_basic PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained1a::test_basic_method PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained1a::test_other PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained1a::test_summary PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained1a::test_summary2 PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained1b::test_basic PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained1b::test_basic_method PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained1b::test_other PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained1c::test_basic PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained1c::test_basic_method PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained1c::test_other PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonNoConstrained::test_basic PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonNoConstrained::test_basic_method PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonNoConstrained::test_other PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained2a::test_basic PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained2a::test_basic_method PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained2a::test_other PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained2b::test_basic PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained2b::test_basic_method PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained2b::test_other PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained2c::test_basic PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained2c::test_basic_method PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestPoissonConstrained2c::test_other PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMPoissonConstrained1a::test_basic PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMPoissonConstrained1a::test_basic_method PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMPoissonConstrained1a::test_other PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMPoissonConstrained1b::test_basic PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMPoissonConstrained1b::test_basic_method PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMPoissonConstrained1b::test_other PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMPoissonConstrained1b::test_compare_glm_poisson PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained1::test_basic PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained1::test_basic_method PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained1::test_other PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained1::test_glm PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestLogitConstrained1::test_basic PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestLogitConstrained1::test_basic_method PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestLogitConstrained1::test_other PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestLogitConstrained1::test_glm SKIPPED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained2::test_basic PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained2::test_basic_method PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained2::test_other PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained2::test_glm PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained2::test_predict PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained2::test_summary PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained2::test_summary2 PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained2::test_fit_constrained_wrap PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained2HC::test_basic PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained2HC::test_basic_method PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained2HC::test_other PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestGLMLogitConstrained2HC::test_glm PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestLogitConstrained2HC::test_basic PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestLogitConstrained2HC::test_basic_method PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestLogitConstrained2HC::test_other PASSED [ 2%] statsmodels/discrete/tests/test_constrained.py::TestLogitConstrained2HC::test_glm SKIPPED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_fit_regularized_invalid_method <- discrete/tests/test_discrete.py PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_params PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_llf PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_conf_int PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_bse PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_aic PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_bic PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_t PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_fit_regularized PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_init_keys PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_null PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_summary PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_fit_regularized_invalid_method <- discrete/tests/test_discrete.py PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_params PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_llf PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_conf_int PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_bse PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_aic PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_bic PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_t PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_init_keys PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_null PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_summary PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_fit_regularized PASSED [ 2%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_fit_regularized_invalid_method <- discrete/tests/test_discrete.py PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_params PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_llf PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_conf_int PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_bse PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_aic PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_bic PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_t PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_fit_regularized PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_init_keys PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_null PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_summary PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_exposure PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_fit_regularized_invalid_method <- discrete/tests/test_discrete.py PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_params PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_llf PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_conf_int PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_bse PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_aic PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_bic PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_t PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_fit_regularized PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_init_keys PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_null PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_summary PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_names PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedPoisson_predict::test_mean PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedPoisson_predict::test_var PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedPoisson_predict::test_predict_prob FAILED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedPoisson_predict::test_predict_options FAILED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_fit_regularized_invalid_method <- discrete/tests/test_discrete.py PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_params PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_llf PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_aic PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_fit_regularized PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_init_keys PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_null PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_summary PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_bse PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_conf_int PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_bic PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_t PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_minimize PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson_predict::test_mean ERROR [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson_predict::test_var ERROR [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson_predict::test_predict_prob ERROR [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_fit_regularized_invalid_method <- discrete/tests/test_discrete.py PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_llf PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_bse PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_aic PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_t PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_init_keys PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_null PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_summary PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_params PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_conf_int PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_bic PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_fit_regularized PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_minimize PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP_predict::test_mean PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP_predict::test_var PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP_predict::test_predict_prob FAILED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP_predict::test_predict_generic_zi FAILED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP_predict2::test_mean PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP_predict2::test_zero_nonzero_mean PASSED [ 3%] statsmodels/discrete/tests/test_count_model.py::TestPandasOffset::test_pd_offset_exposure PASSED [ 3%] statsmodels/discrete/tests/test_diagnostic.py::TestCountDiagnostic::test_count PASSED [ 3%] statsmodels/discrete/tests/test_diagnostic.py::TestCountDiagnostic::test_probs PASSED [ 3%] statsmodels/discrete/tests/test_diagnostic.py::TestPoissonDiagnosticClass::test_spec_tests PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_fit_regularized_invalid_method PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_params PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_conf_int PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_zstat PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_pvalues PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_cov_params PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_llf PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_llnull PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_llr PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_llr_pvalue PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_normalized_cov_params XFAIL [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_bse PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_dof PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_aic PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_bic PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_predict PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_predict_xb PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_loglikeobs PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_jac PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_summary_latex PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_distr PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_pred_table PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_resid_dev PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_resid_generalized PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_resid_response PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNewton::test_init_kwargs PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_fit_regularized_invalid_method PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_params PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_conf_int PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_zstat PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_pvalues PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_cov_params PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_llf PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_llnull PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_llr PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_llr_pvalue PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_normalized_cov_params XFAIL [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_bse PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_dof PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_aic PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_bic PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_predict PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_predict_xb PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_loglikeobs PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_jac PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_summary_latex PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_distr PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_pred_table PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_resid_dev PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_resid_generalized PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitBFGS::test_resid_response PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_fit_regularized_invalid_method PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_params PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_conf_int PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_zstat PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_pvalues PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_cov_params PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_llf PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_llnull PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_llr PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_llr_pvalue PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_normalized_cov_params XFAIL [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_bse PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_dof PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_aic PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_bic PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_predict PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_predict_xb PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_loglikeobs PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_jac PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_summary_latex PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_distr PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_pred_table PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_resid_dev PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_resid_generalized PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitNM::test_resid_response PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_fit_regularized_invalid_method PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_params PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_conf_int PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_zstat PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_pvalues PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_cov_params PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_llf PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_llnull PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_llr PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_llr_pvalue PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_normalized_cov_params XFAIL [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_bse PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_dof PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_aic PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_bic PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_predict PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_predict_xb PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_loglikeobs PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_jac PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_summary_latex PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_distr PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_pred_table PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_resid_dev PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_resid_generalized PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitPowell::test_resid_response PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_fit_regularized_invalid_method PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_params PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_conf_int PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_zstat PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_pvalues PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_cov_params PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_llf PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_llnull PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_llr PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_llr_pvalue PASSED [ 3%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_normalized_cov_params XFAIL [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_bse PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_dof PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_aic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_bic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_predict PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_predict_xb PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_loglikeobs PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_jac PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_summary_latex PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_distr PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_pred_table PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_resid_dev PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_resid_generalized PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitCG::test_resid_response PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_fit_regularized_invalid_method PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_conf_int PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_zstat PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_pvalues PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_cov_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_llf PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_llnull PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_llr PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_llr_pvalue PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_normalized_cov_params XFAIL [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_bse PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_dof PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_aic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_bic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_predict PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_predict_xb PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_loglikeobs PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_jac PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_summary_latex PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_distr PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_pred_table PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_resid_dev PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_resid_generalized PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitNCG::test_resid_response PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_fit_regularized_invalid_method PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_conf_int PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_zstat PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_pvalues PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_cov_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_llf PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_llnull PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_llr PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_llr_pvalue PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_normalized_cov_params XFAIL [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_bse PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_dof PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_aic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_bic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_predict PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_predict_xb PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_loglikeobs PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_jac PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_summary_latex PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_distr PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_pred_table PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_resid_dev PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_resid_generalized PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitBasinhopping::test_resid_response PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_fit_regularized_invalid_method PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_conf_int PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_zstat PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_pvalues PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_cov_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_llf PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_llnull PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_llr PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_llr_pvalue PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_normalized_cov_params XFAIL [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_bse PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_dof PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_aic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_bic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_predict PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_predict_xb PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_loglikeobs PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_jac PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_summary_latex PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_distr PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_pred_table PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_resid_dev PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_resid_generalized PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDefault::test_resid_response PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_fit_regularized_invalid_method PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_conf_int PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_zstat PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_pvalues PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_cov_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_llf PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_llnull PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_llr PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_llr_pvalue PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_normalized_cov_params XFAIL [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_bse PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_dof PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_aic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_bic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_predict PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_predict_xb PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_loglikeobs PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_jac PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_summary_latex PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_distr PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_pred_table PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_resid_dev PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_resid_generalized PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeDogleg::test_resid_response PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_fit_regularized_invalid_method PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_conf_int PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_zstat PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_pvalues PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_cov_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_llf PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_llnull PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_llr PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_llr_pvalue PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_normalized_cov_params XFAIL [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_bse PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_dof PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_aic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_bic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_predict PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_predict_xb PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_loglikeobs PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_jac PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_summary_latex PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_distr PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_pred_table PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_resid_dev PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_resid_generalized PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitMinimizeAdditionalOptions::test_resid_response PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitL1::test_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitL1::test_conf_int PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitL1::test_bse PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitL1::test_nnz_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitL1::test_aic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitL1::test_bic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestProbitL1::test_cov_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitL1::test_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitL1::test_conf_int PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitL1::test_bse PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitL1::test_nnz_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitL1::test_aic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitL1::test_bic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestLogitL1::test_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestLogitL1::test_conf_int PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestLogitL1::test_bse PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestLogitL1::test_nnz_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestLogitL1::test_aic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestLogitL1::test_bic PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestLogitL1::test_cov_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestCVXOPT::test_cvxopt_versus_slsqp SKIPPED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestSweepAlphaL1::test_sweep_alpha PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestPoissonL1Compatability::test_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestPoissonL1Compatability::test_cov_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestPoissonL1Compatability::test_df PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestPoissonL1Compatability::test_t_test PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestPoissonL1Compatability::test_f_test PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestPoissonL1Compatability::test_bad_r_matrix PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialL1Compatability::test_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialL1Compatability::test_cov_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialL1Compatability::test_df PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialL1Compatability::test_t_test PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialL1Compatability::test_f_test PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialL1Compatability::test_bad_r_matrix PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeoL1Compatability::test_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeoL1Compatability::test_cov_params PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeoL1Compatability::test_df PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeoL1Compatability::test_t_test PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeoL1Compatability::test_f_test PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeoL1Compatability::test_bad_r_matrix PASSED [ 4%] statsmodels/discrete/tests/test_discrete.py::TestLogitL1Compatability::test_params PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitL1Compatability::test_cov_params PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitL1Compatability::test_df PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitL1Compatability::test_t_test PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitL1Compatability::test_f_test PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitL1Compatability::test_bad_r_matrix PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitL1Compatability::test_params PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitL1Compatability::test_cov_params PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitL1Compatability::test_df PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitL1Compatability::test_bad_r_matrix PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitL1Compatability::test_t_test PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitL1Compatability::test_f_test SKIPPED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestProbitL1Compatability::test_params PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestProbitL1Compatability::test_cov_params PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestProbitL1Compatability::test_df PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestProbitL1Compatability::test_t_test PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestProbitL1Compatability::test_f_test PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestProbitL1Compatability::test_bad_r_matrix PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestL1AlphaZeroLogit::test_basic_results PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestL1AlphaZeroLogit::test_tests PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestL1AlphaZeroLogit::test_converged PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestL1AlphaZeroProbit::test_basic_results PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestL1AlphaZeroProbit::test_tests PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestL1AlphaZeroMNLogit::test_basic_results PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_dydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_dydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_dydxmedian PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_dydxzero PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_dyexoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_dyexmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_dyexmedian PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_dyexzero PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_eydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_eydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_eydxmedian PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_eydxzero PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_eyexoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_eyexmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_eyexmedian PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_eyexzero PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_dummy_dydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_dummy_dydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_dummy_eydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_dummy_eydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_count_dydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_count_dydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_count_dummy_dydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_count_dummy_dydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_fit_regularized_invalid_method PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_params PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_conf_int PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_zstat PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_pvalues PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_cov_params PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_llf PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_llnull PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_llr PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_llr_pvalue PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_normalized_cov_params XFAIL [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_bse PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_dof PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_aic PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_bic PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_predict PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_predict_xb PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_loglikeobs PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_jac PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_summary_latex PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_distr PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_pred_table PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_resid_dev PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_resid_generalized PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_resid_response PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_resid_pearson PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_exog1 PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_nodummy_exog2 PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_dummy_exog1 PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_dummy_exog2 PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewton::test_diagnostic PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_dydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_dydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_dydxmedian PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_dydxzero PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_dyexoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_dyexmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_dyexmedian PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_dyexzero PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_eydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_eydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_eydxmedian PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_eydxzero PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_eyexoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_eyexmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_eyexmedian PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_eyexzero PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_dummy_dydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_dummy_dydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_dummy_eydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_dummy_eydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_count_dydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_count_dydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_count_dummy_dydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_count_dummy_dydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_resid_pearson PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_exog1 PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_nodummy_exog2 PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_dummy_exog1 PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitNewtonPrepend::test_dummy_exog2 PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_dydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_dydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_dydxmedian PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_dydxzero PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_dyexoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_dyexmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_dyexmedian PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_dyexzero PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_eydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_eydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_eydxmedian PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_eydxzero PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_eyexoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_eyexmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_eyexmedian PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_nodummy_eyexzero PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_dummy_dydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_dummy_dydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_dummy_eydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_dummy_eydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_count_dydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_count_dydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_count_dummy_dydxoverall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_count_dummy_dydxmean PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_fit_regularized_invalid_method PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_params PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_conf_int PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_zstat PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_pvalues PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_cov_params PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_llf PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_llnull PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_llr PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_llr_pvalue PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_normalized_cov_params XFAIL [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_bse PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_dof PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_aic PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_bic PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_predict PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_predict_xb PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_loglikeobs PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_jac PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_summary_latex PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_distr PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_pred_table PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_resid_dev PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_resid_generalized PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestLogitBFGS::test_resid_response PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_fit_regularized_invalid_method PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_params PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_conf_int PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_zstat PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_pvalues PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_llf PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_llnull PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_llr PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_llr_pvalue PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_normalized_cov_params XFAIL [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_bse PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_dof PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_aic PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_bic PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_predict PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_predict_xb PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_loglikeobs PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_jac PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_summary_latex PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_distr PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_margeff_overall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_margeff_dummy_overall PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_resid PASSED [ 5%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_predict_prob PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNewton::test_cov_params XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_fit_regularized_invalid_method PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_cov_params SKIPPED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_llf PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_llnull PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_llr PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_llr_pvalue PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_normalized_cov_params XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_dof PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_aic PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_bic PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_loglikeobs PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_jac PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_summary_latex PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_distr PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_pvalues XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_bse PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_params PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_alpha PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_conf_int PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_zstat PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_fittedvalues PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_predict PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Newton::test_predict_xb PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_fit_regularized_invalid_method PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_cov_params SKIPPED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_llf PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_llnull PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_llr PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_llr_pvalue PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_normalized_cov_params XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_bse PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_dof PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_aic PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_bic PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_loglikeobs PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_jac PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_summary_latex PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_distr PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_pvalues XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_zstat PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_lnalpha PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_params PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_conf_int PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_predict XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Newton::test_predict_xb XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_fit_regularized_invalid_method PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_cov_params SKIPPED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_llf PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_llnull PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_llr PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_llr_pvalue PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_normalized_cov_params XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_dof PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_aic PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_bic PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_loglikeobs PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_jac PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_summary_latex PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_distr PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_pvalues XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_bse PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_params PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_alpha PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_conf_int PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_zstat PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_fittedvalues PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_predict PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2BFGS::test_predict_xb PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_fit_regularized_invalid_method PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_cov_params SKIPPED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_llf PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_llnull PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_llr PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_llr_pvalue PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_normalized_cov_params XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_bse PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_dof PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_aic PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_bic PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_loglikeobs PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_jac PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_summary_latex PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_distr PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_pvalues XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_zstat PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_lnalpha PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_params PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_conf_int PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_predict XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1BFGS::test_predict_xb XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_fit_regularized_invalid_method PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_cov_params SKIPPED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_llnull PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_llr_pvalue PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_normalized_cov_params XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_dof PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_loglikeobs PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_jac PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_summary_latex PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_distr PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_pvalues XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_aic PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_bic PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_conf_int PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_fittedvalues PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_predict PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_params PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_predict_xb PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_zstat PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_llf PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_llr PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialGeometricBFGS::test_bse PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_fit_regularized_invalid_method PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_params PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_conf_int PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_zstat PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_pvalues PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_llf PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_llnull PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_llr PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_llr_pvalue PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_normalized_cov_params XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_bse PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_dof PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_aic PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_bic PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_predict PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_predict_xb PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_loglikeobs PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_jac PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_summary_latex PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_margeff_overall PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_margeff_mean PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_margeff_dummy PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_j PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_k PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_endog_names PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_pred_table PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_resid PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_cov_params XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitNewtonBaseZero::test_distr XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_fit_regularized_invalid_method PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_params PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_conf_int PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_zstat PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_pvalues PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_llf PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_llnull PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_llr PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_llr_pvalue PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_normalized_cov_params XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_bse PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_dof PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_aic PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_bic PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_predict PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_predict_xb PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_loglikeobs PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_jac PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_summary_latex PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_margeff_overall PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_margeff_mean PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_margeff_dummy PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_j PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_k PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_endog_names PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_pred_table PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_resid PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_cov_params XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::TestMNLogitLBFGSBaseZero::test_distr XFAIL [ 6%] statsmodels/discrete/tests/test_discrete.py::test_mnlogit_basinhopping PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::test_perfect_prediction PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::test_poisson_predict PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::test_poisson_newton PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::test_issue_339 PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::test_issue_341 PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::test_negative_binomial_default_alpha_param PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::test_iscount PASSED [ 6%] statsmodels/discrete/tests/test_discrete.py::test_isdummy PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::test_non_binary PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::test_mnlogit_factor PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::test_mnlogit_factor_categorical PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::test_formula_missing_exposure PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::test_predict_with_exposure PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::test_binary_pred_table_zeros PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p2::test_bse PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p2::test_params PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p2::test_alpha PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p2::test_conf_int PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p2::test_aic PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p2::test_bic PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p2::test_df PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p2::test_llf PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p2::test_wald PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p2::test_t PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p2::test_jac PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p2::test_distr PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_transparams::test_bse PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_transparams::test_params PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_transparams::test_alpha PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_transparams::test_conf_int PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_transparams::test_aic PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_transparams::test_bic PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_transparams::test_df PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_transparams::test_llf PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_llf PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_score PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_hessian PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_t PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_init_kwds PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_distr PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_underdispersion::test_basic PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_underdispersion::test_newton PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_underdispersion::test_mean_var PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_underdispersion::test_predict_prob PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_underdispersion::test_jac PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoisson_underdispersion::test_distr PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_fit_regularized_invalid_method PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_cov_params SKIPPED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_llf PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_llnull PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_llr PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_llr_pvalue PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_normalized_cov_params XFAIL [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_dof PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_aic PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_bic PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_loglikeobs PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_jac PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_summary_latex PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_distr PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_pvalues XFAIL [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_bse PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_params PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_alpha PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_conf_int PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_zstat PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_fittedvalues PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_predict PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2Newton::test_predict_xb PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_fit_regularized_invalid_method PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_cov_params SKIPPED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_llf PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_llnull PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_llr PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_llr_pvalue PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_normalized_cov_params XFAIL [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_bse PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_dof PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_aic PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_bic PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_loglikeobs PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_jac PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_summary_latex PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_distr PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_pvalues XFAIL [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_zstat PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_lnalpha PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_params PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_conf_int PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_predict PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1Newton::test_predict_xb PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_fit_regularized_invalid_method PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_cov_params SKIPPED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_llf PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_llnull PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_llr PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_llr_pvalue PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_normalized_cov_params XFAIL [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_dof PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_aic PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_bic PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_loglikeobs PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_jac PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_summary_latex PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_distr PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_pvalues XFAIL [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_bse PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_params PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_alpha PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_conf_int PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_zstat PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_fittedvalues PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_predict PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB2BFGS::test_predict_xb PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_fit_regularized_invalid_method PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_cov_params SKIPPED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_llnull PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_llr_pvalue PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_normalized_cov_params XFAIL [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_dof PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_loglikeobs PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_jac PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_summary_latex PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_distr PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_pvalues XFAIL [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_bse PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_aic PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_bic PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_llf PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_llr PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_zstat PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_lnalpha PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_params PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_conf_int PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_predict PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_predict_xb PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPNB1BFGS::test_init_kwds PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPL1Compatability::test_params PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPL1Compatability::test_cov_params PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPL1Compatability::test_df PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPL1Compatability::test_t_test PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPL1Compatability::test_f_test PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPL1Compatability::test_bad_r_matrix PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPPredictProb::test_predict_prob_p1 PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialPPredictProb::test_predict_prob_p2 PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestPoissonNull::test_llnull PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB1Null::test_llnull PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNB2Null::test_llnull PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNBP2Null::test_llnull PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNBP2Null::test_start_null PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNBP1Null::test_llnull PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestNegativeBinomialNBP1Null::test_start_null PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::TestGeneralizedPoissonNull::test_llnull PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::test_null_options PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::test_optim_kwds_prelim PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::test_unchanging_degrees_of_freedom PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::test_mnlogit_float_name PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::test_cov_confint_pandas PASSED [ 7%] statsmodels/discrete/tests/test_discrete.py::test_mlogit_t_test PASSED [ 7%] statsmodels/discrete/tests/test_margins.py::TestPoissonMargin::test_margins_table PASSED [ 7%] statsmodels/discrete/tests/test_margins.py::TestPoissonMarginDummy::test_margins_table PASSED [ 7%] statsmodels/discrete/tests/test_margins.py::TestNegBinMargin::test_margins_table PASSED [ 7%] statsmodels/discrete/tests/test_margins.py::TestNegBinMarginDummy::test_margins_table PASSED [ 7%] statsmodels/discrete/tests/test_margins.py::TestNegBinPMargin::test_margins_table PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::TestNegativeBinomialPPredict::test_predict_linear PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::TestNegativeBinomialPPredict::test_score_test PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::TestNegativeBinomialPPredict::test_score_test_alpha PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::TestNegativeBinomialPPredict::test_influence PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::TestNegativeBinomialPPredict::test_basic PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::TestNegativeBinomialPPredict::test_predict PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::TestNegativeBinomialPPredict::test_diagnostic PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::TestZINegativeBinomialPPredict::test_basic PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::TestZINegativeBinomialPPredict::test_predict FAILED [ 7%] statsmodels/discrete/tests/test_predict.py::TestZINegativeBinomialPPredict::test_diagnostic FAILED [ 7%] statsmodels/discrete/tests/test_predict.py::TestGeneralizedPoissonPredict::test_predict_linear PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::TestGeneralizedPoissonPredict::test_score_test PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::TestGeneralizedPoissonPredict::test_score_test_alpha PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::TestGeneralizedPoissonPredict::test_influence PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::test_distr[case0] PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::test_distr[case1] PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::test_distr[case2] FAILED [ 7%] statsmodels/discrete/tests/test_predict.py::test_distr[case3] FAILED [ 7%] statsmodels/discrete/tests/test_predict.py::test_distr[case4] FAILED [ 7%] statsmodels/discrete/tests/test_predict.py::test_distr[case5] FAILED [ 7%] statsmodels/discrete/tests/test_predict.py::test_distr[case6] FAILED [ 7%] statsmodels/discrete/tests/test_predict.py::test_distr[case7] PASSED [ 7%] statsmodels/discrete/tests/test_predict.py::test_distr[case8] PASSED [ 8%] statsmodels/discrete/tests/test_predict.py::test_distr[case9] PASSED [ 8%] statsmodels/discrete/tests/test_predict.py::test_distr[case10] PASSED [ 8%] statsmodels/discrete/tests/test_predict.py::test_distr[case11] PASSED [ 8%] statsmodels/discrete/tests/test_predict.py::test_distr[case12] PASSED [ 8%] statsmodels/discrete/tests/test_predict.py::test_distr[case13] PASSED [ 8%] statsmodels/discrete/tests/test_predict.py::test_distr[case14] PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonClu::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonClu::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonClu::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonClu::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluGeneric::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluGeneric::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluGeneric::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluGeneric::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonHC1Generic::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonHC1Generic::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonHC1Generic::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonHC1Generic::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluFit::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluFit::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluFit::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluFit::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluFit::test_basic_inference PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonHC1Fit::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonHC1Fit::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonHC1Fit::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonHC1Fit::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonHC1FitExposure::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonHC1FitExposure::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonHC1FitExposure::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonHC1FitExposure::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluExposure::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluExposure::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluExposure::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluExposure::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluExposureGeneric::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluExposureGeneric::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluExposureGeneric::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestPoissonCluExposureGeneric::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonClu::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonClu::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonClu::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonClu::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonCluGeneric::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonCluGeneric::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonCluGeneric::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonCluGeneric::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonHC1Generic::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonHC1Generic::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonHC1Generic::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonHC1Generic::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonCluFit::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonCluFit::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonCluFit::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonCluFit::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonHC1Fit::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonHC1Fit::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonHC1Fit::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoissonHC1Fit::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinClu::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinClu::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinClu::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinClu::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluExposure::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluExposure::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluExposure::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluExposure::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluGeneric::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluGeneric::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluGeneric::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluGeneric::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluFit::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluFit::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluFit::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluFit::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluExposureFit::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluExposureFit::test_oth PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluExposureFit::test_ttest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestNegbinCluExposureFit::test_waldtest PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoisson::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoisson::test_score_hessian PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoisson::test_score_test PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMPoisson::test_margeff PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMLogit::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMLogit::test_score_hessian PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMLogit::test_score_test PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMLogit::test_margeff PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMLogitOffset::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMLogitOffset::test_score_hessian PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMLogitOffset::test_score_test PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMLogitOffset::test_margeff SKIPPED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMProbit::test_basic PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMProbit::test_score_test PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMProbit::test_margeff PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMProbit::test_score_hessian PASSED [ 8%] statsmodels/discrete/tests/test_sandwich_cov.py::TestGLMProbitOffset::test_basic PASSED [ 8%] 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[ 14%] statsmodels/genmod/tests/test_glm.py::TestTweediePower2::test_params PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweediePower2::test_deviance PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweediePower2::test_df PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweediePower2::test_fittedvalues PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweediePower2::test_summary PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieLog1::test_resid PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieLog1::test_bse PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieLog1::test_params PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieLog1::test_deviance PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieLog1::test_df PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieLog1::test_fittedvalues PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieLog1::test_summary PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieLog15Fair::test_resid PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieLog15Fair::test_bse PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieLog15Fair::test_params PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieLog15Fair::test_deviance PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieLog15Fair::test_df PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieLog15Fair::test_fittedvalues PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieLog15Fair::test_summary PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieSpecialLog0::test_mu PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieSpecialLog0::test_resid PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieSpecialLog1::test_mu PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieSpecialLog1::test_resid PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieSpecialLog2::test_mu PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieSpecialLog2::test_resid PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieSpecialLog3::test_mu PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestTweedieSpecialLog3::test_resid PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_tweedie_EQL PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_tweedie_elastic_net PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_tweedie_EQL_poisson_limit PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_tweedie_EQL_upper_limit PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::testTweediePowerEstimate PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_glm_lasso_6431 PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestRegularized::test_regularized PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestConvergence::test_convergence_atol_only PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestConvergence::test_convergence_rtol_only PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestConvergence::test_convergence_atol_rtol PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestConvergence::test_convergence_atol_only_params PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestConvergence::test_convergence_rtol_only_params PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::TestConvergence::test_convergence_atol_rtol_params PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_poisson_deviance PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_non_invertible_hessian_fails_summary PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_int_scale PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_int_exog[int8] PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_int_exog[int16] PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_int_exog[int32] PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_int_exog[int64] PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_glm_bic PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_glm_bic_warning PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_output_exposure_null PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_qaic PASSED [ 14%] statsmodels/genmod/tests/test_glm.py::test_tweedie_score PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonPlain::test_basic PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonPlain::test_residuals PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonPlain::test_compare_optimizers PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonPlain::test_pearson_chi2 PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonPlain::test_getprediction PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonFwNr::test_basic PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonFwNr::test_residuals PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonFwNr::test_compare_optimizers PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonFwNr::test_pearson_chi2 PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonFwNr::test_getprediction PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonAwNr::test_basic PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonAwNr::test_residuals PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonAwNr::test_compare_optimizers PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonAwNr::test_pearson_chi2 PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonAwNr::test_getprediction PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonPwNr::test_residuals PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonPwNr::test_pearson_chi2 PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonPwNr::test_getprediction PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonPwNr::test_basic XFAIL [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonPwNr::test_compare_optimizers XFAIL [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonFwHC::test_basic PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonFwHC::test_residuals PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonFwHC::test_compare_optimizers PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonFwHC::test_pearson_chi2 PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonFwHC::test_getprediction PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonAwHC::test_basic PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonAwHC::test_residuals PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonAwHC::test_compare_optimizers PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonAwHC::test_pearson_chi2 PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonAwHC::test_getprediction PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonFwClu::test_basic PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonFwClu::test_residuals PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonFwClu::test_compare_optimizers PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonFwClu::test_pearson_chi2 PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmPoissonFwClu::test_getprediction PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmTweedieAwNr::test_basic PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmTweedieAwNr::test_residuals PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmTweedieAwNr::test_compare_optimizers PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmTweedieAwNr::test_pearson_chi2 PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmTweedieAwNr::test_getprediction PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGammaAwNr::test_basic PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGammaAwNr::test_residuals PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGammaAwNr::test_compare_optimizers PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGammaAwNr::test_pearson_chi2 PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGammaAwNr::test_getprediction PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGammaAwNr::test_r_llf PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGaussianAwNr::test_basic PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGaussianAwNr::test_residuals PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGaussianAwNr::test_compare_optimizers PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGaussianAwNr::test_pearson_chi2 PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGaussianAwNr::test_getprediction PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGaussianAwNr::test_r_llf PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::test_wtd_gradient_irls PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestRepeatedvsAggregated::test_basic PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestRepeatedvsAggregated::test_residuals PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestRepeatedvsAggregated::test_compare_optimizers PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestRepeatedvsAggregated::test_pearson_chi2 PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestRepeatedvsAggregated::test_getprediction PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestRepeatedvsAverage::test_basic PASSED [ 14%] statsmodels/genmod/tests/test_glm_weights.py::TestRepeatedvsAverage::test_residuals PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestRepeatedvsAverage::test_compare_optimizers PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestRepeatedvsAverage::test_pearson_chi2 PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestRepeatedvsAverage::test_getprediction PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestTweedieRepeatedvsAggregated::test_basic PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestTweedieRepeatedvsAggregated::test_residuals PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestTweedieRepeatedvsAggregated::test_compare_optimizers PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestTweedieRepeatedvsAggregated::test_pearson_chi2 PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestTweedieRepeatedvsAggregated::test_getprediction PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestTweedieRepeatedvsAverage::test_basic PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestTweedieRepeatedvsAverage::test_residuals PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestTweedieRepeatedvsAverage::test_compare_optimizers PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestTweedieRepeatedvsAverage::test_pearson_chi2 PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestTweedieRepeatedvsAverage::test_getprediction PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestBinomial0RepeatedvsAverage::test_basic PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestBinomial0RepeatedvsAverage::test_residuals PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestBinomial0RepeatedvsAverage::test_compare_optimizers PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestBinomial0RepeatedvsAverage::test_pearson_chi2 PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestBinomial0RepeatedvsAverage::test_getprediction PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestBinomial0RepeatedvsDuplicated::test_basic PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestBinomial0RepeatedvsDuplicated::test_residuals PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestBinomial0RepeatedvsDuplicated::test_compare_optimizers PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestBinomial0RepeatedvsDuplicated::test_pearson_chi2 PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestBinomial0RepeatedvsDuplicated::test_getprediction PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::test_warnings_raised PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::test_weights_different_formats[list] PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::test_weights_different_formats[ndarray] PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::test_weights_different_formats[Series] PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestBinomialVsVarWeights::test_basic PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestBinomialVsVarWeights::test_residuals PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestBinomialVsVarWeights::test_compare_optimizers PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestBinomialVsVarWeights::test_pearson_chi2 PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestBinomialVsVarWeights::test_getprediction PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGaussianWLS::test_basic PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGaussianWLS::test_residuals PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGaussianWLS::test_compare_optimizers PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGaussianWLS::test_pearson_chi2 PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::TestGlmGaussianWLS::test_getprediction PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::test_incompatible_input PASSED [ 15%] statsmodels/genmod/tests/test_glm_weights.py::test_poisson_residuals PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_numdiff[cov_struct0-fam0] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_numdiff[cov_struct0-fam1] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_numdiff[cov_struct0-fam2] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_numdiff[cov_struct1-fam0] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_numdiff[cov_struct1-fam1] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_numdiff[cov_struct1-fam2] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_numdiff[cov_struct2-fam0] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_numdiff[cov_struct2-fam1] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_numdiff[cov_struct2-fam2] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_fit[cov_struct0-fam0] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_fit[cov_struct0-fam1] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_fit[cov_struct0-fam2] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_fit[cov_struct1-fam0] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_fit[cov_struct1-fam1] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_fit[cov_struct1-fam2] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_fit[cov_struct2-fam0] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_fit[cov_struct2-fam1] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_qif_fit[cov_struct2-fam2] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_formula[cov_struct0] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_formula[cov_struct1] PASSED [ 15%] statsmodels/genmod/tests/test_qif.py::test_formula[cov_struct2] PASSED [ 15%] statsmodels/genmod/tests/test_score_test.py::TestScoreTest::test_wald_score PASSED [ 15%] statsmodels/genmod/tests/test_score_test.py::TestScoreTest::test_dispersion PASSED [ 15%] statsmodels/genmod/tests/test_score_test.py::TestScoreTestDispersed::test_wald_score PASSED [ 15%] statsmodels/genmod/tests/test_score_test.py::TestScoreTestDispersed::test_dispersion PASSED [ 15%] statsmodels/genmod/tests/test_score_test.py::TestScoreTestPoisson::test_dispersion PASSED [ 15%] statsmodels/genmod/tests/test_score_test.py::TestScoreTestPoisson::test_wald_score PASSED [ 15%] statsmodels/genmod/tests/test_score_test.py::TestScoreTestPoissonDispersed::test_dispersion PASSED [ 15%] statsmodels/genmod/tests/test_score_test.py::TestScoreTestPoissonDispersed::test_wald_score PASSED [ 15%] statsmodels/genmod/tests/test_score_test.py::TestScoreTestGaussian::test_wald_score PASSED [ 15%] statsmodels/graphics/tests/test_agreement.py::test_mean_diff_plot PASSED [ 15%] statsmodels/graphics/tests/test_boxplots.py::test_violinplot PASSED [ 15%] statsmodels/graphics/tests/test_boxplots.py::test_violinplot_bw_factor PASSED [ 15%] statsmodels/graphics/tests/test_boxplots.py::test_beanplot PASSED [ 15%] statsmodels/graphics/tests/test_boxplots.py::test_beanplot_jitter PASSED [ 15%] statsmodels/graphics/tests/test_boxplots.py::test_beanplot_side_right PASSED [ 15%] statsmodels/graphics/tests/test_boxplots.py::test_beanplot_side_left PASSED [ 15%] statsmodels/graphics/tests/test_boxplots.py::test_beanplot_legend_text PASSED [ 15%] statsmodels/graphics/tests/test_correlation.py::test_plot_corr PASSED [ 15%] statsmodels/graphics/tests/test_correlation.py::test_plot_corr_grid PASSED [ 15%] statsmodels/graphics/tests/test_dotplot.py::test_all PASSED [ 15%] statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_plot_both PASSED [ 15%] statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_plot_rainbow PASSED [ 15%] statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_plot_pandas[str] PASSED [ 15%] statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_plot_pandas[int] PASSED [ 15%] statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_formatting PASSED [ 15%] statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_formatting_errors PASSED [ 15%] statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_plottype PASSED [ 15%] statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_recode_series PASSED [ 15%] statsmodels/graphics/tests/test_functional.py::test_hdr_basic PASSED [ 15%] statsmodels/graphics/tests/test_functional.py::test_hdr_basic_brute PASSED [ 15%] statsmodels/graphics/tests/test_functional.py::test_hdr_plot PASSED [ 15%] statsmodels/graphics/tests/test_functional.py::test_hdr_alpha PASSED [ 15%] statsmodels/graphics/tests/test_functional.py::test_hdr_multiple_alpha PASSED [ 15%] statsmodels/graphics/tests/test_functional.py::test_hdr_threshold PASSED [ 15%] statsmodels/graphics/tests/test_functional.py::test_hdr_bw PASSED [ 15%] statsmodels/graphics/tests/test_functional.py::test_hdr_ncomp PASSED [ 15%] statsmodels/graphics/tests/test_functional.py::test_banddepth_BD2 PASSED [ 15%] statsmodels/graphics/tests/test_functional.py::test_banddepth_MBD PASSED [ 15%] statsmodels/graphics/tests/test_functional.py::test_fboxplot_rainbowplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_qqplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_ppplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_probplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_probplot_exceed PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_qqplot_other_array PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_ppplot_other_array PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_probplot_other_array XFAIL [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_qqplot_other_prbplt PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_ppplot_other_prbplt PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_probplot_other_prbplt XFAIL [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_qqplot_custom_labels PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_ppplot_custom_labels PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_probplot_custom_labels PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_qqplot_pltkwargs PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_ppplot_pltkwargs PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_probplot_pltkwargs PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_fit_params PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_qqplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_ppplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_probplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_probplot_exceed PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_qqplot_other_array PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_ppplot_other_array PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_probplot_other_array XFAIL [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_qqplot_other_prbplt PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_ppplot_other_prbplt PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_probplot_other_prbplt XFAIL [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_qqplot_custom_labels PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_ppplot_custom_labels PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_probplot_custom_labels PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_qqplot_pltkwargs PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_ppplot_pltkwargs PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_probplot_pltkwargs PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_fit_params PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_qqplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_ppplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_probplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_probplot_exceed PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_qqplot_other_array PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_ppplot_other_array PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_probplot_other_array XFAIL [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_qqplot_other_prbplt PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_ppplot_other_prbplt PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_probplot_other_prbplt XFAIL [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_qqplot_custom_labels PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_ppplot_custom_labels PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_probplot_custom_labels PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_qqplot_pltkwargs PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_ppplot_pltkwargs PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_probplot_pltkwargs PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_fit_params PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_qqplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_ppplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_probplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_probplot_exceed PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_qqplot_other_array PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_ppplot_other_array PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_probplot_other_array XFAIL [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_qqplot_other_prbplt PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_ppplot_other_prbplt PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_probplot_other_prbplt XFAIL [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_qqplot_custom_labels PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_ppplot_custom_labels PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_probplot_custom_labels PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_qqplot_pltkwargs PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_ppplot_pltkwargs PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_probplot_pltkwargs PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_fit_params PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_qqplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_ppplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_probplot PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_probplot_exceed PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_qqplot_other_array PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_ppplot_other_array PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_probplot_other_array XFAIL [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_qqplot_other_prbplt PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_ppplot_other_prbplt PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_probplot_other_prbplt XFAIL [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_qqplot_custom_labels PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_ppplot_custom_labels PASSED [ 15%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_probplot_custom_labels PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_qqplot_pltkwargs PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_ppplot_pltkwargs PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_probplot_pltkwargs PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_fit_params PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_loc_set PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_scale_set PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_exceptions PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestCompareSamplesDifferentSize::test_qqplot PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestCompareSamplesDifferentSize::test_ppplot PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_qqplot PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_ppplot PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_probplot PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_probplot_exceed PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_qqplot_other_array PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_ppplot_other_array PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_probplot_other_array XFAIL [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_qqplot_other_prbplt PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_ppplot_other_prbplt PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_probplot_other_prbplt XFAIL [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_qqplot_custom_labels PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_ppplot_custom_labels PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_probplot_custom_labels PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_qqplot_pltkwargs PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_ppplot_pltkwargs PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_probplot_pltkwargs PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_fit_params PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_loc_set PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_scale_set PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_loc_set_in_dist PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_scale_set_in_dist PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestTopLevel::test_qqplot PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestTopLevel::test_qqplot_pltkwargs PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestTopLevel::test_qqplot_2samples_prob_plot_objects PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestTopLevel::test_qqplot_2samples_arrays PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_invalid_dist_config PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_qqplot_unequal PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestCheckDist::test_good PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestCheckDist::test_bad PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestDoPlot::test_baseline PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestDoPlot::test_with_ax PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestDoPlot::test_plot_full_options PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestDoPlot::test_step_baseline PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestDoPlot::test_step_full_options PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestDoPlot::test_plot_qq_line PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestDoPlot::test_step_qq_line PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_badline PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_non45_no_x PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_non45_no_y PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_non45_no_x_no_y PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_45 PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_45_fmt PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_45_fmt_lineoptions PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_r PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_r_fmt PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_r_fmt_lineoptions PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_s PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_s_fmt PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_s_fmt_lineoptions PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_q PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_q_fmt PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestQQLine::test_q_fmt_lineoptions PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestPlottingPosition::test_weibull PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestPlottingPosition::test_lininterp PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestPlottingPosition::test_piecewise PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestPlottingPosition::test_approx_med_unbiased PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::TestPlottingPosition::test_cunnane PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_param_unpacking PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[None-30-30-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[None-30-30-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[None-30-50-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[None-30-50-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[None-50-30-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[None-50-30-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[None-50-50-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[None-50-50-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[45-30-30-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[45-30-30-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[45-30-50-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[45-30-50-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[45-50-30-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[45-50-30-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[45-50-50-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[45-50-50-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[s-30-30-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[s-30-30-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[s-30-50-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[s-30-50-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[s-50-30-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[s-50-30-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[s-50-50-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[s-50-50-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[r-30-30-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[r-30-30-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[r-30-50-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[r-30-50-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[r-50-30-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[r-50-30-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[r-50-50-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[r-50-50-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[q-30-30-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[q-30-30-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[q-30-50-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[q-30-50-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[q-50-30-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[q-50-30-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[q-50-50-labels0] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_correct_labels[q-50-50-labels1] PASSED [ 16%] statsmodels/graphics/tests/test_gofplots.py::test_axis_order PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test_data_conversion PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test_mosaic_simple PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test_mosaic PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test_mosaic_very_complex PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test_axes_labeling PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test_mosaic_empty_cells PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test_recursive_split PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test__reduce_dict PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test__key_splitting PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test_proportion_normalization PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test_false_split PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test_rect_pure_split PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test_rect_deformed_split PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test_gap_split PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test_default_arg_index PASSED [ 16%] statsmodels/graphics/tests/test_mosaicplot.py::test_missing_category PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestPlot::test_plot_fit PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestPlot::test_plot_oth PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestPlot::test_plot_influence PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestPlot::test_plot_leverage_resid2 PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestPlotPandas::test_plot_fit PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestPlotPandas::test_plot_oth PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestPlotPandas::test_plot_influence PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestPlotPandas::test_plot_leverage_resid2 PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestPlotFormula::test_plot_fit PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestPlotFormula::test_plot_oth PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestPlotFormula::test_plot_influence PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestPlotFormula::test_plot_leverage_resid2 PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestPlotFormula::test_one_column_exog PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestABLine::test_abline_model PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestABLine::test_abline_model_ax PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestABLine::test_abline_ab PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestABLine::test_abline_ab_ax PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestABLine::test_abline_remove PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestABLinePandas::test_abline_model PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestABLinePandas::test_abline_model_ax PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestABLinePandas::test_abline_ab PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestABLinePandas::test_abline_ab_ax PASSED [ 16%] statsmodels/graphics/tests/test_regressionplots.py::TestABLinePandas::test_abline_remove PASSED [ 16%] 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statsmodels/multivariate/tests/test_ml_factor.py::test_exact_em PASSED [ 17%] statsmodels/multivariate/tests/test_ml_factor.py::test_fit_ml_em_random_state PASSED [ 17%] statsmodels/multivariate/tests/test_ml_factor.py::test_em PASSED [ 17%] statsmodels/multivariate/tests/test_ml_factor.py::test_1factor PASSED [ 17%][0m statsmodels/multivariate/tests/test_ml_factor.py::test_2factor PASSED [ 17%] statsmodels/multivariate/tests/test_multivariate_ols.py::test_glm_dogs_example PASSED [ 17%] statsmodels/multivariate/tests/test_multivariate_ols.py::test_specify_L_M_by_string PASSED [ 17%] statsmodels/multivariate/tests/test_multivariate_ols.py::test_independent_variable_singular PASSED [ 17%] statsmodels/multivariate/tests/test_multivariate_ols.py::test_from_formula_vs_no_formula PASSED [ 17%] statsmodels/multivariate/tests/test_multivariate_ols.py::test_L_M_matrices_1D_array PASSED [ 18%] statsmodels/multivariate/tests/test_multivariate_ols.py::test_exog_1D_array PASSED [ 18%] statsmodels/multivariate/tests/test_multivariate_ols.py::test_endog_1D_array PASSED [ 18%] statsmodels/multivariate/tests/test_multivariate_ols.py::test_affine_hypothesis PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::TestPCA::test_smoke_plot_and_repr PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::TestPCA::test_eig_svd_equiv PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::TestPCA::test_options PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::TestPCA::test_against_reference PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::TestPCA::test_warnings_and_errors PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::TestPCA::test_pandas PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::TestPCA::test_gls_and_weights PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::TestPCA::test_wide PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::TestPCA::test_projection PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::TestPCA::test_replace_missing PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::TestPCA::test_rsquare PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::TestPCA::test_missing_dataframe PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::TestPCA::test_equivalence PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::TestPCA::test_equivalence_full_matrices PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::test_missing PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::test_too_many_missing PASSED [ 18%] statsmodels/multivariate/tests/test_pca.py::test_gls_warning PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case0] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case1] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case2] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case3] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case4] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case5] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case6] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case7] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case0] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case1] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case2] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case3] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case4] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case5] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case6] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case7] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_weights[case0] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_weights[case1] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_weights[case2] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_weights[case3] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_weights[case4] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_weights[case5] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_weights[case6] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_weights[case7] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsUnit::test_kernels[case0] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsUnit::test_kernels[case1] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsUnit::test_kernels_vectorized[case0] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsUnit::test_kernels_vectorized[case1] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsUnit::test_kernels_weights[case0] PASSED [ 18%] statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsUnit::test_kernels_weights[case1] PASSED [ 18%] statsmodels/nonparametric/tests/test_bandwidths.py::TestBandwidthCalculation::test_calculate_bandwidth_gaussian PASSED [ 18%] statsmodels/nonparametric/tests/test_bandwidths.py::TestBandwidthCalculation::test_calculate_normal_reference_bandwidth PASSED [ 18%] statsmodels/nonparametric/tests/test_bandwidths.py::TestEpanechnikov::test_calculate_normal_reference_constant PASSED [ 18%] statsmodels/nonparametric/tests/test_bandwidths.py::TestGaussian::test_calculate_normal_reference_constant PASSED [ 18%] statsmodels/nonparametric/tests/test_bandwidths.py::TestBiweight::test_calculate_normal_reference_constant PASSED [ 18%] statsmodels/nonparametric/tests/test_bandwidths.py::TestTriweight::test_calculate_normal_reference_constant PASSED [ 18%] statsmodels/nonparametric/tests/test_bandwidths.py::TestAllBandwidthZero::test_bandwidth_zero PASSED [ 18%] statsmodels/nonparametric/tests/test_bandwidths.py::TestAnyBandwidthZero::test_bandwidth_zero PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEExceptions::test_check_is_fit_exception PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEExceptions::test_non_weighted_fft_exception PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEExceptions::test_wrong_weight_length_exception PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEExceptions::test_non_gaussian_fft_exception PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEGauss::test_density PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEGauss::test_evaluate PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEGauss::test_support_gridded PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEGauss::test_cdf_gridded PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEGauss::test_sf_gridded PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEGauss::test_icdf_gridded PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEGaussPandas::test_density PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEGaussPandas::test_evaluate PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEGaussPandas::test_support_gridded PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEGaussPandas::test_cdf_gridded PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEGaussPandas::test_sf_gridded PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEGaussPandas::test_icdf_gridded PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEEpanechnikov::test_density PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEEpanechnikov::test_evaluate PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDETriangular::test_density PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDETriangular::test_evaluate PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEBiweight::test_density PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEBiweight::test_evaluate PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKdeWeights::test_density PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKdeWeights::test_evaluate PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEGaussFFT::test_density PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEGaussFFT::test_evaluate PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWGauss::test_density XFAIL [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWGauss::test_evaluate PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWGauss::test_compare PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWGauss::test_kernel_constants PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWEpa::test_density XFAIL [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWEpa::test_evaluate PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWEpa::test_compare PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWEpa::test_kernel_constants PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWTri::test_density XFAIL [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWTri::test_evaluate PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWTri::test_compare PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWTri::test_kernel_constants PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWBiw::test_density XFAIL [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWBiw::test_evaluate PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWBiw::test_compare PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWBiw::test_kernel_constants PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos::test_density XFAIL [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos::test_evaluate SKIPPED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos::test_compare PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos::test_kernel_constants PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos2::test_density XFAIL [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos2::test_evaluate PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos2::test_compare PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos2::test_kernel_constants PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestNormConstant::test_norm_constant_calculation PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::test_kde_bw_positive PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::test_fit_self PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDECustomBandwidth::test_check_is_fit_ok_with_custom_bandwidth PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDECustomBandwidth::test_check_is_fit_ok_with_standard_custom_bandwidth PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDECustomBandwidth::test_check_is_fit_ok_with_float_bandwidth[True] PASSED [ 18%] statsmodels/nonparametric/tests/test_kde.py::TestKDECustomBandwidth::test_check_is_fit_ok_with_float_bandwidth[False] PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEUnivariate::test_pdf_non_fft PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEUnivariate::test_weighted_pdf_non_fft PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEUnivariate::test_all_samples_same_location_bw PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEUnivariate::test_int PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_pdf_mixeddata_CV_LS PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_pdf_mixeddata_LS_vs_ML PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_pdf_mixeddata_CV_ML PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_pdf_continuous PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_pdf_ordered PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_unordered_CV_LS PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_continuous_cdf PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_mixeddata_cdf PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_continuous_cvls_efficient PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_continuous_cvml_efficient PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_efficient_notrandom PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_efficient_user_specified_bw PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_mixeddata_CV_LS PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_continuous_CV_ML PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_unordered_CV_LS PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_pdf_continuous PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_pdf_mixeddata PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_continuous_normal_ref PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_continuous_cdf PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_mixeddata_cdf PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_continuous_cvml_efficient PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_efficient_user_specified_bw PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::test_all_kernels[biw] PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::test_all_kernels[cos] PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::test_all_kernels[epa] PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::test_all_kernels[gau] PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::test_all_kernels[tri] PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::test_all_kernels[triw] PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_density.py::test_all_kernels[uni] PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_ordered_lc_cvls PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_continuousdata_lc_cvls PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_continuousdata_ll_cvls PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_continuous_mfx_ll_cvls PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_mixed_mfx_ll_cvls PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_mfx_nonlinear_ll_cvls XFAIL [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_continuous_cvls_efficient PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_censored_ll_cvls PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_continuous_lc_aic PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_significance_continuous PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_significance_discrete PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_user_specified_kernel PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_censored_user_specified_kernel PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_efficient_user_specificed_bw PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_censored_efficient_user_specificed_bw PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::test_invalid_bw PASSED [ 18%] statsmodels/nonparametric/tests/test_kernel_regression.py::test_invalid_kernel PASSED [ 18%] statsmodels/nonparametric/tests/test_kernels.py::TestEpan::test_smoothconf PASSED [ 18%] statsmodels/nonparametric/tests/test_kernels.py::TestEpan::test_smoothconf_data PASSED [ 18%] statsmodels/nonparametric/tests/test_kernels.py::TestGau::test_smoothconf PASSED [ 18%] statsmodels/nonparametric/tests/test_kernels.py::TestGau::test_smoothconf_data PASSED [ 18%] statsmodels/nonparametric/tests/test_kernels.py::TestUniform::test_smoothconf PASSED [ 18%] statsmodels/nonparametric/tests/test_kernels.py::TestUniform::test_smoothconf_data PASSED [ 18%] statsmodels/nonparametric/tests/test_kernels.py::TestTriangular::test_smoothconf PASSED [ 18%] statsmodels/nonparametric/tests/test_kernels.py::TestTriangular::test_smoothconf_data PASSED [ 18%] statsmodels/nonparametric/tests/test_kernels.py::TestCosine::test_smoothconf_data PASSED [ 18%] statsmodels/nonparametric/tests/test_kernels.py::TestCosine::test_smoothconf XFAIL [ 18%] statsmodels/nonparametric/tests/test_kernels.py::TestBiweight::test_smoothconf PASSED [ 18%] statsmodels/nonparametric/tests/test_kernels.py::TestBiweight::test_smoothconf_data PASSED [ 18%] statsmodels/nonparametric/tests/test_kernels.py::test_tricube PASSED [ 18%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_import PASSED [ 18%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_flat[False] PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_flat[True] PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_range PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_simple PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_iter_0 PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_iter_0_3 PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_frac_2_3 PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_frac_1_5 PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_delta_0 PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_delta_rdef PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_delta_1 PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_options PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_duplicate_xs PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_spike PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_exog_predict PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::test_returns_inputs PASSED [ 19%] statsmodels/nonparametric/tests/test_lowess.py::test_xvals_dtype PASSED [ 19%] statsmodels/othermod/tests/test_beta.py::TestBetaModel::test_income_coefficients PASSED [ 19%] statsmodels/othermod/tests/test_beta.py::TestBetaModel::test_income_precision PASSED [ 19%] statsmodels/othermod/tests/test_beta.py::TestBetaModel::test_methylation_coefficients PASSED [ 19%] statsmodels/othermod/tests/test_beta.py::TestBetaModel::test_methylation_precision PASSED [ 19%] statsmodels/othermod/tests/test_beta.py::TestBetaModel::test_precision_formula PASSED [ 19%] statsmodels/othermod/tests/test_beta.py::TestBetaModel::test_scores PASSED [ 19%] statsmodels/othermod/tests/test_beta.py::TestBetaModel::test_results_other PASSED [ 19%] statsmodels/othermod/tests/test_beta.py::TestBetaMeth::test_basic PASSED [ 19%] statsmodels/othermod/tests/test_beta.py::TestBetaMeth::test_resid PASSED [ 19%] statsmodels/othermod/tests/test_beta.py::TestBetaMeth::test_oim PASSED [ 19%] statsmodels/othermod/tests/test_beta.py::TestBetaMeth::test_predict_distribution PASSED [ 19%] statsmodels/othermod/tests/test_beta.py::TestBetaIncome::test_score_test PASSED [ 19%] statsmodels/othermod/tests/test_beta.py::TestBetaIncome::test_influence PASSED [ 19%] statsmodels/regression/tests/test_cov.py::test_HC_use PASSED [ 19%] statsmodels/regression/tests/test_dimred.py::test_poisson PASSED [ 19%] statsmodels/regression/tests/test_dimred.py::test_sir_regularized_numdiff PASSED [ 19%] statsmodels/regression/tests/test_dimred.py::test_sir_regularized_1d PASSED [ 19%] statsmodels/regression/tests/test_dimred.py::test_sir_regularized_2d PASSED [ 19%] statsmodels/regression/tests/test_dimred.py::test_covreduce PASSED [ 19%] statsmodels/regression/tests/test_glsar_gretl.py::TestGLSARGretl::test_all PASSED [ 19%] statsmodels/regression/tests/test_glsar_gretl.py::test_GLSARlag PASSED [ 19%] statsmodels/regression/tests/test_glsar_stata.py::TestGLSARCorc::test_params_table PASSED [ 19%] statsmodels/regression/tests/test_glsar_stata.py::TestGLSARCorc::test_predicted PASSED [ 19%] statsmodels/regression/tests/test_glsar_stata.py::TestGLSARCorc::test_rho PASSED [ 19%] statsmodels/regression/tests/test_glsar_stata.py::TestGLSARCorc::test_glsar_arima PASSED [ 19%] statsmodels/regression/tests/test_glsar_stata.py::TestGLSARCorc::test_glsar_iter0 PASSED [ 19%] statsmodels/regression/tests/test_lme.py::TestMixedLM::test_compare_numdiff[False-False-False] PASSED [ 19%] statsmodels/regression/tests/test_lme.py::TestMixedLM::test_compare_numdiff[False-False-True] PASSED [ 19%] 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statsmodels/robust/tests/test_rlm.py::TestHampel::test_params PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestHampel::test_standarderrors PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestHampel::test_confidenceintervals SKIPPED [ 25%] statsmodels/robust/tests/test_rlm.py::TestHampel::test_scale PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestHampel::test_weights PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestHampel::test_residuals PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestHampel::test_degrees PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestHampel::test_bcov_unscaled PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestHampel::test_bcov_scaled PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestHampel::test_tvalues PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestHampel::test_tpvalues PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestHampel::test_summary PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestHampel::test_summary2 PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestHampel::test_chisq PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestHampel::test_predict PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquare::test_params PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquare::test_standarderrors PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquare::test_confidenceintervals SKIPPED [ 25%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquare::test_scale PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquare::test_weights PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquare::test_residuals PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquare::test_degrees PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquare::test_bcov_unscaled PASSED [ 25%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquare::test_bcov_scaled PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquare::test_tvalues PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquare::test_tpvalues PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquare::test_summary PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquare::test_summary2 PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquare::test_chisq PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquare::test_predict PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrews::test_params PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrews::test_standarderrors PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrews::test_confidenceintervals PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrews::test_scale PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrews::test_weights PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrews::test_residuals PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrews::test_degrees PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrews::test_bcov_unscaled SKIPPED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrews::test_bcov_scaled PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrews::test_tvalues SKIPPED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrews::test_tpvalues PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrews::test_summary PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrews::test_summary2 PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrews::test_chisq PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrews::test_predict PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmHuber::test_params PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmHuber::test_standarderrors PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmHuber::test_confidenceintervals PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmHuber::test_scale PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmHuber::test_weights PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmHuber::test_residuals PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmHuber::test_degrees PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmHuber::test_bcov_unscaled SKIPPED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmHuber::test_bcov_scaled PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmHuber::test_tvalues SKIPPED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmHuber::test_tpvalues PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestHampelHuber::test_params PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestHampelHuber::test_standarderrors PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestHampelHuber::test_confidenceintervals PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestHampelHuber::test_scale PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestHampelHuber::test_weights PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestHampelHuber::test_residuals PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestHampelHuber::test_degrees PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestHampelHuber::test_bcov_unscaled SKIPPED [ 26%] statsmodels/robust/tests/test_rlm.py::TestHampelHuber::test_bcov_scaled PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestHampelHuber::test_tvalues SKIPPED [ 26%] statsmodels/robust/tests/test_rlm.py::TestHampelHuber::test_tpvalues PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestHampelHuber::test_summary PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestHampelHuber::test_summary2 PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestHampelHuber::test_chisq PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestHampelHuber::test_predict PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquareHuber::test_params PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquareHuber::test_standarderrors PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquareHuber::test_confidenceintervals PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquareHuber::test_scale PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquareHuber::test_weights PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquareHuber::test_residuals PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquareHuber::test_degrees PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquareHuber::test_bcov_unscaled SKIPPED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquareHuber::test_bcov_scaled PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquareHuber::test_tvalues SKIPPED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquareHuber::test_tpvalues PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquareHuber::test_summary PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquareHuber::test_summary2 PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquareHuber::test_chisq PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmBisquareHuber::test_predict PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrewsHuber::test_params PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrewsHuber::test_standarderrors PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrewsHuber::test_confidenceintervals PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrewsHuber::test_scale PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrewsHuber::test_weights PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrewsHuber::test_residuals PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrewsHuber::test_degrees PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrewsHuber::test_bcov_unscaled SKIPPED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrewsHuber::test_bcov_scaled PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrewsHuber::test_tvalues SKIPPED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrewsHuber::test_tpvalues PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrewsHuber::test_summary PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrewsHuber::test_summary2 PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrewsHuber::test_chisq PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmAndrewsHuber::test_predict PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_params PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_standarderrors PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_confidenceintervals SKIPPED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_scale PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_weights PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_residuals PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_degrees PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_bcov_unscaled PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_bcov_scaled PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_tvalues PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_tpvalues PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_missing PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_rlm_start_values PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_rlm_start_values_errors PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_perfect_fit[AndrewWave] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_perfect_const[AndrewWave] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_perfect_fit[LeastSquares] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_perfect_const[LeastSquares] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_perfect_fit[HuberT] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_perfect_const[HuberT] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_perfect_fit[TrimmedMean] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_perfect_const[TrimmedMean] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_perfect_fit[TukeyBiweight] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_perfect_const[TukeyBiweight] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_perfect_fit[Hampel] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_perfect_const[Hampel] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_perfect_fit[RamsayE] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_perfect_const[RamsayE] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_alt_criterion[weights] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_alt_criterion[coefs] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_alt_criterion[sresid] PASSED [ 26%] statsmodels/robust/tests/test_rlm.py::test_bad_criterion PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestChem::test_mean PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestChem::test_median PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestChem::test_mad PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestChem::test_iqr PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestChem::test_qn PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestChem::test_huber_scale PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestChem::test_huber_location PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestChem::test_huber_huberT PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestChem::test_huber_Hampel PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestMad::test_mad PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestMad::test_mad_empty PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestMad::test_mad_center PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestMadAxes::test_axis0 PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestMadAxes::test_axis1 PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestMadAxes::test_axis2 PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestMadAxes::test_axisneg1 PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestIqr::test_iqr PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestIqr::test_iqr_empty PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestIqrAxes::test_axis0 PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestIqrAxes::test_axis1 PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestIqrAxes::test_axis2 PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestIqrAxes::test_axisneg1 PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestQn::test_qn_naive PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestQn::test_qn_robustbase PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestQn::test_qn_empty PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestQnAxes::test_axis0 PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestQnAxes::test_axis1 PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestQnAxes::test_axis2 PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestQnAxes::test_axisneg1 PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestHuber::test_huber_result_shape PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestHuberAxes::test_default PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestHuberAxes::test_axis1 PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestHuberAxes::test_axis2 PASSED [ 26%] statsmodels/robust/tests/test_scale.py::TestHuberAxes::test_axisneg1 PASSED [ 26%] statsmodels/robust/tests/test_scale.py::test_mad_axis_none PASSED [ 26%] statsmodels/sandbox/distributions/tests/test_extras.py::test_skewnorm PASSED [ 26%] statsmodels/sandbox/distributions/tests/test_extras.py::test_skewt PASSED [ 26%] statsmodels/sandbox/distributions/tests/test_gof_new.py::test_loop_vectorized_batch_equivalence PASSED [ 26%] statsmodels/sandbox/distributions/tests/test_multivariate.py::Test_MVN_MVT_prob::test_mvn_mvt_1 SKIPPED [ 26%] statsmodels/sandbox/distributions/tests/test_multivariate.py::Test_MVN_MVT_prob::test_mvn_mvt_2 SKIPPED [ 26%] statsmodels/sandbox/distributions/tests/test_multivariate.py::Test_MVN_MVT_prob::test_mvn_mvt_3 SKIPPED [ 26%] statsmodels/sandbox/distributions/tests/test_multivariate.py::Test_MVN_MVT_prob::test_mvn_mvt_4 SKIPPED [ 26%] statsmodels/sandbox/distributions/tests/test_multivariate.py::Test_MVN_MVT_prob::test_mvn_mvt_5 SKIPPED [ 26%] statsmodels/sandbox/distributions/tests/test_multivariate.py::TestMVDistributions::test_mvn_pdf PASSED [ 26%] statsmodels/sandbox/distributions/tests/test_multivariate.py::TestMVDistributions::test_mvt_pdf PASSED [ 26%] statsmodels/sandbox/distributions/tests/test_norm_expan.py::TestExpandNormMom::test_dist1 PASSED [ 26%] statsmodels/sandbox/distributions/tests/test_norm_expan.py::TestExpandNormMom::test_cdf_ppf_roundtrip PASSED [ 26%] statsmodels/sandbox/distributions/tests/test_norm_expan.py::TestExpandNormMom::test_pdf PASSED [ 26%] statsmodels/sandbox/distributions/tests/test_norm_expan.py::TestExpandNormMom::test_mvsk PASSED [ 26%] statsmodels/sandbox/distributions/tests/test_norm_expan.py::TestExpandNormSample::test_ks PASSED [ 26%] statsmodels/sandbox/distributions/tests/test_norm_expan.py::TestExpandNormSample::test_mvsk PASSED [ 26%] statsmodels/sandbox/distributions/tests/test_transf.py::Test_Transf2::test_equivalent PASSED [ 26%] statsmodels/sandbox/distributions/tests/test_transf.py::Test_Transf2::test_equivalent_negsq PASSED [ 26%] statsmodels/sandbox/nonparametric/tests/test_kernel_extras.py::TestSemiLinear::test_basic PASSED [ 26%] statsmodels/sandbox/nonparametric/tests/test_smoothers.py::TestPolySmoother1::test_predict PASSED [ 26%] statsmodels/sandbox/nonparametric/tests/test_smoothers.py::TestPolySmoother1::test_coef PASSED [ 26%] statsmodels/sandbox/nonparametric/tests/test_smoothers.py::TestPolySmoother1::test_df PASSED [ 26%] statsmodels/sandbox/nonparametric/tests/test_smoothers.py::TestPolySmoother2::test_predict PASSED [ 26%] statsmodels/sandbox/nonparametric/tests/test_smoothers.py::TestPolySmoother2::test_coef PASSED [ 26%] statsmodels/sandbox/nonparametric/tests/test_smoothers.py::TestPolySmoother2::test_df PASSED [ 26%] statsmodels/sandbox/nonparametric/tests/test_smoothers.py::TestPolySmoother3::test_predict PASSED [ 26%] statsmodels/sandbox/nonparametric/tests/test_smoothers.py::TestPolySmoother3::test_coef PASSED [ 26%] statsmodels/sandbox/nonparametric/tests/test_smoothers.py::TestPolySmoother3::test_df PASSED [ 26%] statsmodels/sandbox/panel/tests/test_random_panel.py::test_short_panel PASSED [ 26%] statsmodels/sandbox/regression/tests/test_gmm.py::test_iv2sls_r PASSED [ 26%] statsmodels/sandbox/regression/tests/test_gmm.py::test_ivgmm0_r PASSED [ 26%] statsmodels/sandbox/regression/tests/test_gmm.py::test_ivgmm1_stata PASSED [ 26%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMOLS::test_basic PASSED [ 26%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMOLS::test_other XFAIL [ 26%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMSt1::test_basic PASSED [ 26%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMSt1::test_other PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMSt1::test_hypothesis PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMSt1::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMSt1::test_use_t PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStTwostep::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStTwostep::test_other PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStTwostep::test_hypothesis PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStTwostep::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStTwostep::test_use_t PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStTwostepNO::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStTwostepNO::test_other PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStTwostepNO::test_hypothesis PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStTwostepNO::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStTwostepNO::test_use_t PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOnestep::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOnestep::test_hypothesis PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOnestep::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOnestep::test_use_t PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOnestep::test_bse_other PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOnestep::test_other XFAIL [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOnestepNO::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOnestepNO::test_hypothesis PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOnestepNO::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOnestepNO::test_use_t PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOnestepNO::test_other XFAIL [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiter::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiter::test_hypothesis PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiter::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiter::test_use_t PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiter::test_other XFAIL [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiter::test_bse_other PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO::test_hypothesis PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO::test_use_t PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO::test_other XFAIL [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Linear::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Linear::test_hypothesis PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Linear::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Linear::test_use_t PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Linear::test_other XFAIL [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Nonlinear::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Nonlinear::test_hypothesis PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Nonlinear::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Nonlinear::test_use_t PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Nonlinear::test_other XFAIL [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Nonlinear::test_score PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterOLS_Linear::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterOLS_Linear::test_hypothesis PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterOLS_Linear::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterOLS_Linear::test_use_t PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterOLS_Linear::test_other XFAIL [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMSt2::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestIV2SLSSt1::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestIV2SLSSt1::test_other PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestIV2SLSSt1::test_hypothesis PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestIV2SLSSt1::test_hausman PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestIV2SLSSt1::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::TestIV2SLSSt1::test_input_dimensions PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::test_noconstant PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm.py::test_gmm_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMAddOnestep::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMAddOnestep::test_other PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMAddOnestep::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMAddTwostep::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMAddTwostep::test_other PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMAddTwostep::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultOnestep::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultOnestep::test_other PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultOnestep::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostep::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostep::test_other PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostep::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostepDefault::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostepDefault::test_other PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostepDefault::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostepCenter::test_basic PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostepCenter::test_other PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostepCenter::test_summary PASSED [ 27%] statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostepCenter::test_more PASSED [ 27%] statsmodels/sandbox/stats/tests/test_multicomp.py::test_tukey_pvalues SKIPPED [ 27%] statsmodels/sandbox/stats/tests/test_runs.py::test_mean_cutoff PASSED [ 27%] statsmodels/sandbox/stats/tests/test_runs.py::test_median_cutoff PASSED [ 27%] statsmodels/sandbox/stats/tests/test_runs.py::test_numeric_cutoff PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestAdditiveModel::test_predict PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestAdditiveModel::test_params PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestAdditiveModel::test_df XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestAdditiveModel::test_fitted PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMPoisson::test_predict PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMPoisson::test_fitted XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMPoisson::test_params PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMPoisson::test_df XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMPoisson::test_mu PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMPoisson::test_prediction PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMBinomial::test_predict PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMBinomial::test_fitted XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMBinomial::test_params PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMBinomial::test_df XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMBinomial::test_mu PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMBinomial::test_prediction PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMGaussianLogLink::test_predict XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMGaussianLogLink::test_fitted XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMGaussianLogLink::test_params XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMGaussianLogLink::test_df XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMGaussianLogLink::test_mu XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMGaussianLogLink::test_prediction XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMGamma::test_predict PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMGamma::test_fitted XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMGamma::test_params PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMGamma::test_df XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMGamma::test_mu PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMGamma::test_prediction PASSED [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMNegativeBinomial::test_predict XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMNegativeBinomial::test_params XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMNegativeBinomial::test_mu XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMNegativeBinomial::test_prediction XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMNegativeBinomial::test_fitted XFAIL [ 27%] statsmodels/sandbox/tests/test_gam.py::TestGAMNegativeBinomial::test_df XFAIL [ 27%] statsmodels/sandbox/tests/test_pca.py::test_pca_princomp PASSED [ 27%] statsmodels/sandbox/tests/test_pca.py::test_pca_svd PASSED [ 27%] statsmodels/sandbox/tests/test_predict_functional.py::TestPredFunc::test_formula PASSED [ 27%] statsmodels/sandbox/tests/test_predict_functional.py::TestPredFunc::test_lm_contrast PASSED [ 27%] statsmodels/sandbox/tests/test_predict_functional.py::TestPredFunc::test_glm_formula_contrast PASSED [ 27%] statsmodels/sandbox/tests/test_predict_functional.py::TestPredFunc::test_scb PASSED [ 27%] statsmodels/sandbox/tests/test_predict_functional.py::TestPredFunc::test_glm_formula PASSED [ 27%] statsmodels/sandbox/tests/test_predict_functional.py::TestPredFunc::test_noformula_prediction PASSED [ 27%] statsmodels/stats/libqsturng/tests/test_qsturng.py::TestQsturng::test_scalar PASSED [ 27%] statsmodels/stats/libqsturng/tests/test_qsturng.py::TestQsturng::test_vector PASSED [ 27%] statsmodels/stats/libqsturng/tests/test_qsturng.py::TestQsturng::test_invalid_parameters PASSED [ 27%] statsmodels/stats/libqsturng/tests/test_qsturng.py::TestQsturng::test_handful_to_tbl PASSED [ 27%] statsmodels/stats/libqsturng/tests/test_qsturng.py::TestQsturng::test_all_to_tbl SKIPPED [ 27%] statsmodels/stats/libqsturng/tests/test_qsturng.py::TestQsturng::test_handful_to_ch PASSED [ 27%] statsmodels/stats/libqsturng/tests/test_qsturng.py::TestQsturng::test_10000_to_ch PASSED [ 27%] statsmodels/stats/libqsturng/tests/test_qsturng.py::TestPsturng::test_scalar PASSED [ 27%] statsmodels/stats/libqsturng/tests/test_qsturng.py::TestPsturng::test_vector PASSED [ 27%] statsmodels/stats/libqsturng/tests/test_qsturng.py::TestPsturng::test_v_equal_one PASSED [ 27%] statsmodels/stats/libqsturng/tests/test_qsturng.py::TestPsturng::test_invalid_parameters PASSED [ 27%] statsmodels/stats/libqsturng/tests/test_qsturng.py::TestPsturng::test_handful_to_known_values PASSED [ 27%] statsmodels/stats/libqsturng/tests/test_qsturng.py::TestPsturng::test_100_random_values PASSED [ 27%] statsmodels/stats/tests/test_anova.py::TestAnovaLM::test_results PASSED [ 27%] statsmodels/stats/tests/test_anova.py::TestAnovaLMNoconstant::test_results PASSED [ 27%] statsmodels/stats/tests/test_anova.py::TestAnovaLMCompare::test_results PASSED [ 27%] statsmodels/stats/tests/test_anova.py::TestAnovaLMCompareNoconstant::test_results PASSED [ 27%] statsmodels/stats/tests/test_anova.py::TestAnova2::test_results PASSED [ 27%] statsmodels/stats/tests/test_anova.py::TestAnova2Noconstant::test_results PASSED [ 27%] statsmodels/stats/tests/test_anova.py::TestAnova2HC0::test_results PASSED [ 27%] statsmodels/stats/tests/test_anova.py::TestAnova2HC1::test_results PASSED [ 27%] statsmodels/stats/tests/test_anova.py::TestAnova2HC2::test_results PASSED [ 27%] statsmodels/stats/tests/test_anova.py::TestAnova2HC3::test_results PASSED [ 27%] statsmodels/stats/tests/test_anova.py::TestAnova3::test_results PASSED [ 27%] statsmodels/stats/tests/test_anova.py::TestAnova3HC0::test_results PASSED [ 27%] statsmodels/stats/tests/test_anova.py::TestAnova3HC1::test_results PASSED [ 27%] statsmodels/stats/tests/test_anova.py::TestAnova3HC2::test_results PASSED [ 27%] statsmodels/stats/tests/test_anova.py::TestAnova3HC3::test_results PASSED [ 27%] statsmodels/stats/tests/test_anova_rm.py::test_single_factor_repeated_measures_anova PASSED [ 27%] statsmodels/stats/tests/test_anova_rm.py::test_two_factors_repeated_measures_anova PASSED [ 27%] statsmodels/stats/tests/test_anova_rm.py::test_three_factors_repeated_measures_anova PASSED [ 27%] statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_invalid_factor_name PASSED [ 27%] statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_collinearity PASSED [ 27%] statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_unbalanced_data PASSED [ 27%] statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_aggregation PASSED [ 27%] statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_aggregation_one_subject_duplicated PASSED [ 27%] statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_aggregate_func PASSED [ 27%] statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_aggregate_func_mean PASSED [ 27%] statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_aggregate_compare_with_ezANOVA PASSED [ 27%] statsmodels/stats/tests/test_base.py::test_holdertuple PASSED [ 27%] statsmodels/stats/tests/test_base.py::test_holdertuple2 PASSED [ 27%] statsmodels/stats/tests/test_contingency_tables.py::test_homogeneity PASSED [ 27%] statsmodels/stats/tests/test_contingency_tables.py::test_SquareTable_from_data PASSED [ 27%] statsmodels/stats/tests/test_contingency_tables.py::test_SquareTable_nonsquare PASSED [ 27%] statsmodels/stats/tests/test_contingency_tables.py::test_cumulative_odds PASSED [ 27%] statsmodels/stats/tests/test_contingency_tables.py::test_local_odds PASSED [ 27%] statsmodels/stats/tests/test_contingency_tables.py::test_shifting PASSED [ 27%] statsmodels/stats/tests/test_contingency_tables.py::test_stratified_table_cube PASSED [ 27%] statsmodels/stats/tests/test_contingency_tables.py::test_resids PASSED [ 27%] statsmodels/stats/tests/test_contingency_tables.py::test_ordinal_association PASSED [ 27%] statsmodels/stats/tests/test_contingency_tables.py::test_chi2_association PASSED [ 27%] statsmodels/stats/tests/test_contingency_tables.py::test_symmetry PASSED [ 27%] statsmodels/stats/tests/test_contingency_tables.py::test_mcnemar PASSED [ 27%] statsmodels/stats/tests/test_contingency_tables.py::test_from_data_stratified PASSED [ 27%] statsmodels/stats/tests/test_contingency_tables.py::test_from_data_2x2 PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::test_cochranq PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified1::test_oddsratio_pooled PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified1::test_logodds_pooled PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified1::test_null_odds PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified1::test_oddsratio_pooled_confint PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified1::test_logodds_pooled_confint PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified1::test_equal_odds PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified1::test_pandas PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified1::test_from_data PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified2::test_oddsratio_pooled PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified2::test_logodds_pooled PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified2::test_null_odds PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified2::test_oddsratio_pooled_confint PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified2::test_logodds_pooled_confint PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified2::test_equal_odds PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified2::test_pandas PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified2::test_from_data PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified3::test_oddsratio_pooled PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified3::test_logodds_pooled PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified3::test_null_odds PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified3::test_oddsratio_pooled_confint PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified3::test_logodds_pooled_confint PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified3::test_equal_odds PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified3::test_pandas PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::TestStratified3::test_from_data PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::Test2x2_1::test_oddsratio PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::Test2x2_1::test_log_oddsratio PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::Test2x2_1::test_log_oddsratio_se PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::Test2x2_1::test_oddsratio_pvalue PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::Test2x2_1::test_oddsratio_confint PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::Test2x2_1::test_riskratio PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::Test2x2_1::test_log_riskratio PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::Test2x2_1::test_log_riskratio_se PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::Test2x2_1::test_riskratio_pvalue PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::Test2x2_1::test_riskratio_confint PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::Test2x2_1::test_log_riskratio_confint PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::Test2x2_1::test_from_data PASSED [ 28%] statsmodels/stats/tests/test_contingency_tables.py::Test2x2_1::test_summary PASSED [ 28%] statsmodels/stats/tests/test_contrast.py::TestContrast::test_contrast1 PASSED [ 28%] statsmodels/stats/tests/test_contrast.py::TestContrast::test_contrast2 PASSED [ 28%] statsmodels/stats/tests/test_contrast.py::TestContrast::test_contrast3 PASSED [ 28%] statsmodels/stats/tests/test_contrast.py::TestContrast::test_estimable PASSED [ 28%] statsmodels/stats/tests/test_contrast.py::test_constraints PASSED [ 28%] statsmodels/stats/tests/test_correlation.py::test_kernel_covariance PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::test_corr_psd PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::TestCovPSD::test_cov_nearest PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::TestCorrPSD1::test_nearest PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::TestCorrPSD1::test_clipped PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::TestCorrPSD1::test_cov_nearest PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::test_corrpsd_threshold[0] PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::test_corrpsd_threshold[1e-15] PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::test_corrpsd_threshold[1e-10] PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::test_corrpsd_threshold[1e-06] PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::Test_Factor::test_corr_nearest_factor_arrpack PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::Test_Factor::test_corr_nearest_factor[1] PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::Test_Factor::test_corr_nearest_factor[2] PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::Test_Factor::test_corr_nearest_factor_sparse[1] PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::Test_Factor::test_corr_nearest_factor_sparse[2] PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::Test_Factor::test_spg_optim PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::Test_Factor::test_decorrelate PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::Test_Factor::test_logdet PASSED [ 28%] statsmodels/stats/tests/test_corrpsd.py::Test_Factor::test_solve PASSED [ 28%] 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statsmodels/stats/tests/test_diagnostic.py::test_acorr_lm_smoke[cov1-2-False] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_acorr_lm_smoke_no_autolag PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_order_by[None-0.25] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_order_by[None-0.5] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_order_by[None-0.75] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_order_by[order_by1-0.25] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_order_by[order_by1-0.5] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_order_by[order_by1-0.75] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_order_by[order_by2-0.25] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_order_by[order_by2-0.5] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_order_by[order_by2-0.75] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_order_by[x0-0.25] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_order_by[x0-0.5] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_order_by[x0-0.75] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_order_by[order_by4-0.25] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_order_by[order_by4-0.5] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_order_by[order_by4-0.75] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_centered[None] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_centered[0.33] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_smoke_centered[300] PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_rainbow_exception PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_small_skip PASSED [ 29%] statsmodels/stats/tests/test_diagnostic.py::test_diagnostics_pandas PASSED [ 29%] statsmodels/stats/tests/test_diagnostic_other.py::TestCMTOLS::test_score PASSED [ 29%] statsmodels/stats/tests/test_diagnostic_other.py::TestCMTOLS::test_scorehc0 PASSED [ 29%] statsmodels/stats/tests/test_diagnostic_other.py::TestCMTOLS::test_scoreopg PASSED [ 29%] statsmodels/stats/tests/test_dist_dependant_measures.py::TestDistDependenceMeasures::test_input_validation_nobs PASSED [ 29%] statsmodels/stats/tests/test_dist_dependant_measures.py::TestDistDependenceMeasures::test_input_validation_unknown_method PASSED [ 29%] statsmodels/stats/tests/test_dist_dependant_measures.py::TestDistDependenceMeasures::test_statistic_value_asym_method PASSED [ 29%] 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statsmodels/stats/tests/test_dist_dependant_measures.py::TestDistDependenceMeasures::test_dvar PASSED [ 29%] statsmodels/stats/tests/test_effectsize.py::test_noncent_chi2 PASSED [ 29%] statsmodels/stats/tests/test_effectsize.py::test_noncent_f PASSED [ 29%] statsmodels/stats/tests/test_effectsize.py::test_noncent_t PASSED [ 29%] statsmodels/stats/tests/test_gof.py::test_chisquare_power PASSED [ 29%] statsmodels/stats/tests/test_gof.py::test_chisquare PASSED [ 29%] statsmodels/stats/tests/test_gof.py::test_chisquare_effectsize PASSED [ 29%] statsmodels/stats/tests/test_groups_sw.py::TestBalanced::test_values PASSED [ 29%] statsmodels/stats/tests/test_groups_sw.py::TestBalanced::test_raises PASSED [ 29%] statsmodels/stats/tests/test_groups_sw.py::TestUnBalanced::test_values PASSED [ 29%] statsmodels/stats/tests/test_groups_sw.py::TestUnBalanced::test_raises PASSED [ 29%] statsmodels/stats/tests/test_influence.py::test_influence_glm_bernoulli PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceLogitGLMMLE::test_basics PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceLogitGLMMLE::test_plots PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceLogitGLMMLE::test_summary PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceLogitGLMMLE::test_looo PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceBinomialGLMMLE::test_basics PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceBinomialGLMMLE::test_plots PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceBinomialGLMMLE::test_summary PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceBinomialGLMMLE::test_looo PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceBinomialGLMMLE::test_r PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceGaussianGLMMLE::test_basics PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceGaussianGLMMLE::test_plots PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceGaussianGLMMLE::test_summary PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceGaussianGLMMLE::test_looo PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceGaussianGLMOLS::test_plots PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceGaussianGLMOLS::test_basics PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceGaussianGLMOLS::test_summary PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceLogitCompare::test_basics PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceLogitCompare::test_plots PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceLogitCompare::test_summary PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceProbitCompare::test_basics PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceProbitCompare::test_plots PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceProbitCompare::test_summary PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluenceProbitCompare::test_basics_specific PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluencePoissonCompare::test_basics PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluencePoissonCompare::test_plots PASSED [ 29%] statsmodels/stats/tests/test_influence.py::TestInfluencePoissonCompare::test_summary PASSED [ 29%] statsmodels/stats/tests/test_inter_rater.py::test_fleiss_kappa PASSED [ 29%] statsmodels/stats/tests/test_inter_rater.py::test_fleis_randolph PASSED [ 29%] statsmodels/stats/tests/test_inter_rater.py::TestUnweightedCohens::test_results PASSED [ 29%] statsmodels/stats/tests/test_inter_rater.py::TestUnweightedCohens::test_option PASSED [ 29%] statsmodels/stats/tests/test_inter_rater.py::TestWeightedCohens::test_results PASSED [ 29%] statsmodels/stats/tests/test_inter_rater.py::TestWeightedCohens::test_option PASSED [ 29%] statsmodels/stats/tests/test_inter_rater.py::test_cohenskappa_weights PASSED [ 29%] statsmodels/stats/tests/test_inter_rater.py::test_cohens_kappa_irr PASSED [ 29%] statsmodels/stats/tests/test_inter_rater.py::test_fleiss_kappa_irr PASSED [ 29%] statsmodels/stats/tests/test_inter_rater.py::test_to_table PASSED [ 29%] statsmodels/stats/tests/test_inter_rater.py::test_aggregate_raters PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_equi FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_sdp PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester0-49] FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester0-50] FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester1-49] FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester1-50] FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester2-49] FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester2-50] FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester3-49] FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester3-50] FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester4-49] FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester4-50] FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester5-49] FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester5-50] FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[sdp-tester0-49] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[sdp-tester0-50] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[sdp-tester1-49] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[sdp-tester1-50] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[sdp-tester2-49] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[sdp-tester2-50] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[sdp-tester3-49] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[sdp-tester3-50] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[sdp-tester4-49] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[sdp-tester4-50] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[sdp-tester5-49] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_testers[sdp-tester5-50] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_sim[tester0-300-100-6-equi] FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_sim[tester0-300-100-6-sdp] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_sim[tester1-300-100-3.5-equi] FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_sim[tester1-300-100-3.5-sdp] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_sim[tester2-300-100-3.5-equi] FAILED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_sim[tester2-300-100-3.5-sdp] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_sim[tester3-3000-200-3.5-equi] PASSED [ 29%] statsmodels/stats/tests/test_knockoff.py::test_sim[tester3-3000-200-3.5-sdp] PASSED [ 29%] statsmodels/stats/tests/test_lilliefors.py::TestLilliefors::test_normal PASSED [ 29%] statsmodels/stats/tests/test_lilliefors.py::TestLilliefors::test_normal_table PASSED [ 29%] statsmodels/stats/tests/test_lilliefors.py::TestLilliefors::test_expon PASSED [ 29%] statsmodels/stats/tests/test_lilliefors.py::TestLilliefors::test_pval_bounds PASSED [ 29%] statsmodels/stats/tests/test_lilliefors.py::TestLilliefors::test_min_nobs PASSED [ 29%] statsmodels/stats/tests/test_lilliefors.py::TestLilliefors::test_large_sample PASSED [ 29%] statsmodels/stats/tests/test_lilliefors.py::TestLilliefors::test_x_dims PASSED [ 29%] statsmodels/stats/tests/test_lilliefors.py::test_get_lilliefors_errors PASSED [ 29%] statsmodels/stats/tests/test_lilliefors.py::test_ksstat PASSED [ 29%] statsmodels/stats/tests/test_mediation.py::test_framing_example PASSED [ 29%] statsmodels/stats/tests/test_mediation.py::test_framing_example_moderator PASSED [ 29%] statsmodels/stats/tests/test_mediation.py::test_framing_example_formula PASSED [ 29%] statsmodels/stats/tests/test_mediation.py::test_framing_example_moderator_formula PASSED [ 29%] statsmodels/stats/tests/test_mediation.py::test_surv PASSED [ 29%] statsmodels/stats/tests/test_meta.py::TestEffectsizeBinom::test_effectsize PASSED [ 29%] statsmodels/stats/tests/test_meta.py::TestEffSmdMeta::test_smd PASSED [ 29%] statsmodels/stats/tests/test_meta.py::TestMetaK1::test_tau_kacker PASSED [ 29%] statsmodels/stats/tests/test_meta.py::TestMetaK1::test_pm PASSED [ 29%] statsmodels/stats/tests/test_meta.py::TestMetaK1::test_dl PASSED [ 29%] statsmodels/stats/tests/test_meta.py::TestMetaBinOR::test_basic PASSED [ 29%] statsmodels/stats/tests/test_meta.py::TestMetaBinOR::test_plot PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_cov2corr PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion[mom0] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion[mom1] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion[mom2] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion[mom3] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion[mom4] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion[mom5] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion[mom6] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion[mom7] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion[mom8] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion[mom9] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion[mom10] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_multidimensional[test_vals0] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_multidimensional[test_vals1] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion_types[cum2mc0] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion_types[cum2mc1] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion_types[mc2cum] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion_types[mc2mnc] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion_types[mc2mvsk] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion_types[mnc2cum] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion_types[mnc2mc0] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion_types[mnc2mc1] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion_types[mvsk2mc] PASSED [ 29%] statsmodels/stats/tests/test_moment_helpers.py::test_moment_conversion_types[mvsk2mnc] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests1::test_multi_pvalcorrection_rmethods[BH-val0] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests1::test_multi_pvalcorrection_rmethods[BY-val1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests1::test_multi_pvalcorrection_rmethods[Bonferroni-val2] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests1::test_multi_pvalcorrection_rmethods[Hochberg-val3] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests1::test_multi_pvalcorrection_rmethods[Holm-val4] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests1::test_multi_pvalcorrection_rmethods[SidakSD-val5] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests1::test_multi_pvalcorrection_rmethods[SidakSS-val6] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests1::test_multi_pvalcorrection_rmethods[TSBH_0.05-val7] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests1::test_multi_pvalcorrection_rmethods[rawp-val8] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests1::test_multi_pvalcorrection PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests2::test_multi_pvalcorrection_rmethods[BH-val0] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests2::test_multi_pvalcorrection_rmethods[BY-val1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests2::test_multi_pvalcorrection_rmethods[Bonferroni-val2] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests2::test_multi_pvalcorrection_rmethods[Hochberg-val3] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests2::test_multi_pvalcorrection_rmethods[Holm-val4] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests2::test_multi_pvalcorrection_rmethods[SidakSD-val5] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests2::test_multi_pvalcorrection_rmethods[SidakSS-val6] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests2::test_multi_pvalcorrection_rmethods[TSBH_0.05-val7] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests2::test_multi_pvalcorrection_rmethods[rawp-val8] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests2::test_multi_pvalcorrection PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests3::test_multi_pvalcorrection_rmethods[BH-val0] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests3::test_multi_pvalcorrection_rmethods[BY-val1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests3::test_multi_pvalcorrection_rmethods[Bonferroni-val2] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests3::test_multi_pvalcorrection_rmethods[Hochberg-val3] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests3::test_multi_pvalcorrection_rmethods[Holm-val4] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests3::test_multi_pvalcorrection_rmethods[SidakSD-val5] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests3::test_multi_pvalcorrection_rmethods[SidakSS-val6] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests3::test_multi_pvalcorrection_rmethods[TSBH_0.05-val7] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests3::test_multi_pvalcorrection_rmethods[rawp-val8] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests3::test_multi_pvalcorrection PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests4::test_multi_pvalcorrection_rmethods[BH-val0] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests4::test_multi_pvalcorrection_rmethods[BY-val1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests4::test_multi_pvalcorrection_rmethods[Bonferroni-val2] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests4::test_multi_pvalcorrection_rmethods[Hochberg-val3] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests4::test_multi_pvalcorrection_rmethods[Holm-val4] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests4::test_multi_pvalcorrection_rmethods[SidakSD-val5] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests4::test_multi_pvalcorrection_rmethods[SidakSS-val6] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests4::test_multi_pvalcorrection_rmethods[TSBH_0.05-val7] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests4::test_multi_pvalcorrection_rmethods[rawp-val8] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::TestMultiTests4::test_multi_pvalcorrection PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-b-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-b-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-b-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-s-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-s-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-s-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-sh-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-sh-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-sh-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-hs-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-hs-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-hs-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-h-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-h-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-h-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-hommel-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-hommel-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-hommel-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-fdr_i-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-fdr_i-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-fdr_i-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-fdr_n-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-fdr_n-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-fdr_n-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-fdr_tsbky-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-fdr_tsbky-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-fdr_tsbky-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-fdr_tsbh-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-fdr_tsbh-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-fdr_tsbh-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-fdr_gbs-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-fdr_gbs-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[0-fdr_gbs-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-b-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-b-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-b-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-s-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-s-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-s-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-sh-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-sh-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-sh-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-hs-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-hs-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-hs-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-h-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-h-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-h-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-hommel-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-hommel-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-hommel-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-fdr_i-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-fdr_i-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-fdr_i-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-fdr_n-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-fdr_n-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-fdr_n-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-fdr_tsbky-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-fdr_tsbky-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-fdr_tsbky-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-fdr_tsbh-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-fdr_tsbh-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-fdr_tsbh-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-fdr_gbs-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-fdr_gbs-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[1-fdr_gbs-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-b-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-b-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-b-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-s-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-s-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-s-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-sh-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-sh-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-sh-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-hs-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-hs-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-hs-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-h-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-h-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-h-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-hommel-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-hommel-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-hommel-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-fdr_i-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-fdr_i-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-fdr_i-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-fdr_n-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-fdr_n-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-fdr_n-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-fdr_tsbky-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-fdr_tsbky-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-fdr_tsbky-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-fdr_tsbh-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-fdr_tsbh-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-fdr_tsbh-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-fdr_gbs-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-fdr_gbs-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[2-fdr_gbs-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-b-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-b-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-b-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-s-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-s-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-s-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-sh-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-sh-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-sh-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-hs-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-hs-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-hs-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-h-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-h-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-h-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-hommel-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-hommel-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-hommel-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-fdr_i-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-fdr_i-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-fdr_i-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-fdr_n-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-fdr_n-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-fdr_n-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-fdr_tsbky-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-fdr_tsbky-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-fdr_tsbky-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-fdr_tsbh-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-fdr_tsbh-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-fdr_tsbh-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-fdr_gbs-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-fdr_gbs-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[3-fdr_gbs-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-b-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-b-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-b-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-s-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-s-0.05] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-s-0.1] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-sh-0.01] PASSED [ 30%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-sh-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-sh-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-hs-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-hs-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-hs-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-h-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-h-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-h-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-hommel-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-hommel-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-hommel-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-fdr_i-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-fdr_i-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-fdr_i-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-fdr_n-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-fdr_n-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-fdr_n-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-fdr_tsbky-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-fdr_tsbky-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-fdr_tsbky-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-fdr_tsbh-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-fdr_tsbh-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-fdr_tsbh-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-fdr_gbs-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-fdr_gbs-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[4-fdr_gbs-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-b-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-b-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-b-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-s-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-s-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-s-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-sh-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-sh-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-sh-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-hs-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-hs-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-hs-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-h-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-h-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-h-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-hommel-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-hommel-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-hommel-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-fdr_i-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-fdr_i-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-fdr_i-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-fdr_n-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-fdr_n-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-fdr_n-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-fdr_tsbky-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-fdr_tsbky-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-fdr_tsbky-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-fdr_tsbh-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-fdr_tsbh-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-fdr_tsbh-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-fdr_gbs-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-fdr_gbs-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[5-fdr_gbs-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-b-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-b-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-b-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-s-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-s-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-s-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-sh-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-sh-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-sh-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-hs-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-hs-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-hs-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-h-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-h-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-h-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-hommel-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-hommel-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-hommel-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-fdr_i-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-fdr_i-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-fdr_i-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-fdr_n-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-fdr_n-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-fdr_n-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-fdr_tsbky-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-fdr_tsbky-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-fdr_tsbky-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-fdr_tsbh-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-fdr_tsbh-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-fdr_tsbh-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-fdr_gbs-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-fdr_gbs-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[6-fdr_gbs-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-b-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-b-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-b-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-s-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-s-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-s-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-sh-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-sh-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-sh-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-hs-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-hs-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-hs-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-h-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-h-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-h-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-hommel-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-hommel-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-hommel-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-fdr_i-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-fdr_i-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-fdr_i-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-fdr_n-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-fdr_n-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-fdr_n-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-fdr_tsbky-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-fdr_tsbky-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-fdr_tsbky-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-fdr_tsbh-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-fdr_tsbh-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-fdr_tsbh-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-fdr_gbs-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-fdr_gbs-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[7-fdr_gbs-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-b-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-b-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-b-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-s-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-s-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-s-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-sh-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-sh-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-sh-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-hs-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-hs-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-hs-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-h-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-h-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-h-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-hommel-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-hommel-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-hommel-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-fdr_i-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-fdr_i-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-fdr_i-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-fdr_n-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-fdr_n-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-fdr_n-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-fdr_tsbky-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-fdr_tsbky-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-fdr_tsbky-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-fdr_tsbh-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-fdr_tsbh-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-fdr_tsbh-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-fdr_gbs-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-fdr_gbs-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[8-fdr_gbs-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-b-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-b-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-b-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-s-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-s-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-s-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-sh-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-sh-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-sh-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-hs-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-hs-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-hs-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-h-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-h-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-h-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-hommel-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-hommel-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-hommel-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-fdr_i-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-fdr_i-0.05] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-fdr_i-0.1] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-fdr_n-0.01] PASSED [ 31%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-fdr_n-0.05] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-fdr_n-0.1] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-fdr_tsbky-0.01] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-fdr_tsbky-0.05] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-fdr_tsbky-0.1] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-fdr_tsbh-0.01] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-fdr_tsbh-0.05] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-fdr_tsbh-0.1] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-fdr_gbs-0.01] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-fdr_gbs-0.05] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[9-fdr_gbs-0.1] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-b-0.01] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-b-0.05] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-b-0.1] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-s-0.01] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-s-0.05] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-s-0.1] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-sh-0.01] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-sh-0.05] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-sh-0.1] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-hs-0.01] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-hs-0.05] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-hs-0.1] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-h-0.01] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-h-0.05] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-h-0.1] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-hommel-0.01] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-hommel-0.05] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-hommel-0.1] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-fdr_i-0.01] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-fdr_i-0.05] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-fdr_i-0.1] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-fdr_n-0.01] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-fdr_n-0.05] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-fdr_n-0.1] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-fdr_tsbky-0.01] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-fdr_tsbky-0.05] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-fdr_tsbky-0.1] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-fdr_tsbh-0.01] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-fdr_tsbh-0.05] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-fdr_tsbh-0.1] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-fdr_gbs-0.01] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-fdr_gbs-0.05] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_pvalcorrection_reject[10-fdr_gbs-0.1] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_hommel PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_fdr_bky PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_fdr_twostage PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_issorted[b] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_issorted[fdr_bh] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_issorted[fdr_by] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_issorted[fdr_gbs] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_issorted[fdr_tsbh] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_issorted[fdr_tsbky] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_issorted[h] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_issorted[ho] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_issorted[hs] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_issorted[s] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_issorted[sh] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_floating_precision[b] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_floating_precision[fdr_bh] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_floating_precision[fdr_by] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_floating_precision[fdr_gbs] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_floating_precision[fdr_tsbh] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_floating_precision[fdr_tsbky] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_floating_precision[h] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_floating_precision[ho] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_floating_precision[hs] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_floating_precision[s] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_floating_precision[sh] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_tukeyhsd PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_local_fdr PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_null_distribution PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_null_constrained[True-True-True] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_null_constrained[True-True-False] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_null_constrained[True-False-True] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_null_constrained[True-False-False] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_null_constrained[False-True-True] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_null_constrained[False-True-False] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_null_constrained[False-False-True] PASSED [ 32%] statsmodels/stats/tests/test_multi.py::test_null_constrained[False-False-False] PASSED [ 32%] statsmodels/stats/tests/test_multivariate.py::test_mv_mean PASSED [ 32%] statsmodels/stats/tests/test_multivariate.py::test_mvmean_2indep PASSED [ 32%] statsmodels/stats/tests/test_multivariate.py::test_confint_simult PASSED [ 32%] statsmodels/stats/tests/test_multivariate.py::TestCovStructure::test_spherical PASSED [ 32%] statsmodels/stats/tests/test_multivariate.py::TestCovStructure::test_diagonal PASSED [ 32%] statsmodels/stats/tests/test_multivariate.py::TestCovStructure::test_blockdiagonal PASSED [ 32%] statsmodels/stats/tests/test_multivariate.py::TestCovStructure::test_covmat PASSED [ 32%] statsmodels/stats/tests/test_multivariate.py::test_cov_oneway PASSED [ 32%] statsmodels/stats/tests/test_nonparametric.py::test_mcnemar_exact PASSED [ 32%] statsmodels/stats/tests/test_nonparametric.py::test_mcnemar_chisquare PASSED [ 32%] statsmodels/stats/tests/test_nonparametric.py::test_mcnemar_vectorized PASSED [ 32%] statsmodels/stats/tests/test_nonparametric.py::test_symmetry_bowker PASSED [ 32%] statsmodels/stats/tests/test_nonparametric.py::test_cochransq PASSED [ 32%] statsmodels/stats/tests/test_nonparametric.py::test_cochransq2 PASSED [ 32%] statsmodels/stats/tests/test_nonparametric.py::test_cochransq3 PASSED [ 32%] statsmodels/stats/tests/test_nonparametric.py::test_runstest PASSED [ 32%] statsmodels/stats/tests/test_nonparametric.py::test_runstest_2sample PASSED [ 32%] statsmodels/stats/tests/test_nonparametric.py::test_brunnermunzel_one_sided PASSED [ 32%] statsmodels/stats/tests/test_nonparametric.py::test_brunnermunzel_two_sided PASSED [ 32%] statsmodels/stats/tests/test_nonparametric.py::test_rank_compare_2indep1 PASSED [ 32%] statsmodels/stats/tests/test_nonparametric.py::test_rank_compare_ord PASSED [ 32%] statsmodels/stats/tests/test_nonparametric.py::test_rank_compare_vectorized PASSED [ 32%] statsmodels/stats/tests/test_oaxaca.py::TestOaxaca::test_results PASSED [ 32%] statsmodels/stats/tests/test_oaxaca.py::TestOaxacaNoSwap::test_results PASSED [ 32%] statsmodels/stats/tests/test_oaxaca.py::TestOaxacaPandas::test_results PASSED [ 32%] statsmodels/stats/tests/test_oaxaca.py::TestOaxacaPandasNoSwap::test_results PASSED [ 32%] statsmodels/stats/tests/test_oaxaca.py::TestOaxacaNoConstPassed::test_results PASSED [ 32%] statsmodels/stats/tests/test_oaxaca.py::TestOaxacaNoSwapNoConstPassed::test_results PASSED [ 32%] statsmodels/stats/tests/test_oaxaca.py::TestOaxacaPandasNoConstPassed::test_results PASSED [ 32%] statsmodels/stats/tests/test_oaxaca.py::TestOaxacaPandasNoSwapNoConstPassed::test_results PASSED [ 32%] statsmodels/stats/tests/test_oaxaca.py::TestOneModel::test_results PASSED [ 32%] statsmodels/stats/tests/test_oaxaca.py::TestZeroModel::test_results PASSED [ 32%] statsmodels/stats/tests/test_oaxaca.py::TestOmegaModel::test_results PASSED [ 32%] statsmodels/stats/tests/test_oaxaca.py::TestPooledModel::test_results PASSED [ 32%] statsmodels/stats/tests/test_oneway.py::test_oneway_effectsize PASSED [ 32%] statsmodels/stats/tests/test_oneway.py::test_effectsize_power PASSED [ 32%] statsmodels/stats/tests/test_oneway.py::test_effectsize_fstat PASSED [ 32%] statsmodels/stats/tests/test_oneway.py::test_effectsize_fstat_stata PASSED [ 32%] statsmodels/stats/tests/test_oneway.py::test_scale_transform[median] PASSED [ 32%] statsmodels/stats/tests/test_oneway.py::test_scale_transform[mean] PASSED [ 32%] statsmodels/stats/tests/test_oneway.py::test_scale_transform[trimmed] PASSED [ 32%] statsmodels/stats/tests/test_oneway.py::TestOnewayEquivalenc::test_equivalence_equal PASSED [ 32%] statsmodels/stats/tests/test_oneway.py::TestOnewayEquivalenc::test_equivalence_welch PASSED [ 32%] statsmodels/stats/tests/test_oneway.py::TestOnewayScale::test_means PASSED [ 32%] statsmodels/stats/tests/test_oneway.py::TestOnewayScale::test_levene PASSED [ 32%] statsmodels/stats/tests/test_oneway.py::TestOnewayScale::test_options PASSED [ 32%] statsmodels/stats/tests/test_oneway.py::TestOnewayScale::test_equivalence PASSED [ 32%] statsmodels/stats/tests/test_oneway.py::TestOnewayOLS::test_ols_noncentrality PASSED [ 32%] statsmodels/stats/tests/test_oneway.py::test_simulate_equivalence PASSED [ 32%] statsmodels/stats/tests/test_outliers_influence.py::test_reset_stata PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2::test_multicomptukey PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2::test_group_tukey PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2::test_shortcut_function PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2::test_plot_simultaneous_ci PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2::test_table_names_default_group_order PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2::test_table_names_custom_group_order PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2Pandas::test_multicomptukey PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2Pandas::test_group_tukey PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2Pandas::test_shortcut_function PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2Pandas::test_plot_simultaneous_ci PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2Pandas::test_table_names_default_group_order PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2Pandas::test_table_names_custom_group_order PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2Pandas::test_incorrect_output PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2s::test_multicomptukey PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2s::test_group_tukey PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2s::test_shortcut_function PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD2s::test_plot_simultaneous_ci PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD3::test_multicomptukey PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD3::test_group_tukey PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD3::test_shortcut_function PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD3::test_plot_simultaneous_ci PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD4::test_multicomptukey PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD4::test_group_tukey PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD4::test_shortcut_function PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD4::test_plot_simultaneous_ci PASSED [ 32%] statsmodels/stats/tests/test_pairwise.py::TestTuckeyHSD4::test_hochberg_intervals PASSED [ 32%] statsmodels/stats/tests/test_panel_robustcov.py::test_panel_robust_cov PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS1::test_power PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS1::test_positional PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS1::test_roots PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS1::test_power_plot PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS2::test_power PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS2::test_positional PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS2::test_roots PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS2::test_power_plot PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS3::test_power PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS3::test_positional PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS3::test_roots PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS3::test_power_plot PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS4::test_power PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS4::test_positional PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS4::test_roots PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS4::test_power_plot PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS5::test_power PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS5::test_positional PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS5::test_roots PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS5::test_power_plot PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS6::test_power PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS6::test_positional PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS6::test_roots PASSED [ 32%] statsmodels/stats/tests/test_power.py::TestTTPowerOneS6::test_power_plot PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS1::test_power PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS1::test_positional PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS1::test_roots PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS1::test_power_plot PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS2::test_power PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS2::test_positional PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS2::test_roots PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS2::test_power_plot PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS3::test_power PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS3::test_positional PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS3::test_roots PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS3::test_power_plot PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS4::test_power PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS4::test_positional PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS4::test_roots PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS4::test_power_plot PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS5::test_power PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS5::test_positional PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS5::test_roots PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS5::test_power_plot PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS6::test_power PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS6::test_positional PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS6::test_roots PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestTTPowerTwoS6::test_power_plot PASSED [ 33%] statsmodels/stats/tests/test_power.py::test_normal_power_explicit PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower1::test_power PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower1::test_positional PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower1::test_roots PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower1::test_power_plot PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower2::test_power PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower2::test_positional PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower2::test_roots PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower2::test_power_plot PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower_onesamp1::test_power PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower_onesamp1::test_positional PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower_onesamp1::test_roots PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower_onesamp1::test_power_plot PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower_onesamp2::test_power PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower_onesamp2::test_positional PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower_onesamp2::test_roots PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestNormalIndPower_onesamp2::test_power_plot PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestChisquarePower::test_power PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestChisquarePower::test_roots PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestChisquarePower::test_power_plot PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestChisquarePower::test_positional PASSED [ 33%] statsmodels/stats/tests/test_power.py::test_ftest_power PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestFtestAnovaPower::test_power PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestFtestAnovaPower::test_positional PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestFtestAnovaPower::test_roots PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestFtestAnovaPower::test_power_plot PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestFtestPower::test_power PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestFtestPower::test_positional PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestFtestPower::test_roots PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestFtestPower::test_power_plot SKIPPED [ 33%] statsmodels/stats/tests/test_power.py::TestFtestPower::test_kwargs PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestFtestPowerF2::test_power PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestFtestPowerF2::test_positional PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestFtestPowerF2::test_roots PASSED [ 33%] statsmodels/stats/tests/test_power.py::TestFtestPowerF2::test_power_plot SKIPPED [ 33%] statsmodels/stats/tests/test_power.py::test_power_solver PASSED [ 33%] statsmodels/stats/tests/test_power.py::test_power_solver_warn XFAIL [ 33%] statsmodels/stats/tests/test_power.py::test_normal_sample_size_one_tail PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[agresti_coull-case0] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[agresti_coull-case1] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[agresti_coull-case2] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[agresti_coull-case3] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[agresti_coull-case4] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[agresti_coull-case5] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[agresti_coull-case6] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[agresti_coull-case7] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[normal-case0] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[normal-case1] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[normal-case2] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[normal-case3] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[normal-case4] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[normal-case5] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[normal-case6] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[normal-case7] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[beta-case0] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[beta-case1] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[beta-case2] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[beta-case3] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[beta-case4] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[beta-case5] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[beta-case6] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[beta-case7] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[wilson-case0] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[wilson-case1] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[wilson-case2] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[wilson-case3] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[wilson-case4] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[wilson-case5] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[wilson-case6] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[wilson-case7] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[jeffreys-case0] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[jeffreys-case1] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[jeffreys-case2] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[jeffreys-case3] SKIPPED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[jeffreys-case4] SKIPPED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[jeffreys-case5] SKIPPED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[jeffreys-case6] SKIPPED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion[jeffreys-case7] SKIPPED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion_ndim[agresti_coull] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion_ndim[normal] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion_ndim[beta] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion_ndim[wilson] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_proportion_ndim[jeffreys] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_samplesize_confidenceinterval_prop PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_proportion_effect_size PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_multinomial_proportions PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_multinomial_proportions_errors PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_multinomial_proportions_zeros PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::TestProportion::test_proptest PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::TestProportion::test_pairwiseproptest PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::TestProportion::test_number_pairs_1493 PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::TestProportion::test_default_values PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::TestProportion::test_scalar PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_binom_test PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_binom_rejection_interval PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_binom_tost PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_power_binom_tost PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_power_ztost_prop PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ztost PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_power_ztost_prop_norm PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_proportion_ztests PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_2indep PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_confint_2indep_propcis PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_score_test_2indep PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_test_2indep PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_equivalence_2indep PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_score_confint_koopman_nam PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_power_2indep PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count0] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count1] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count2] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count3] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count4] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count5] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count6] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count7] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count8] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count9] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count10] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count11] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count12] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count13] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count14] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count15] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count0] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count1] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count2] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count3] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count4] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count5] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count6] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count7] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count8] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count9] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count10] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count11] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count12] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count13] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count14] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count15] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count0] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count1] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count2] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count3] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count4] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count5] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count6] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count7] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count8] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count9] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count10] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count11] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count12] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count13] PASSED [ 33%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count14] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count15] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count0] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count1] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count2] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count3] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count4] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count5] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count6] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count7] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count8] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count9] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count10] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count11] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count12] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count13] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count14] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count15] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count0] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count1] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count2] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count3] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count4] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count5] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count6] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count7] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count8] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count9] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count10] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count11] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count12] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count13] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count14] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count15] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count0] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count1] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count2] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count3] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count4] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count5] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count6] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count7] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count8] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count9] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count10] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count11] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count12] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count13] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count14] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count15] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count0] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count1] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count2] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count3] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count4] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count5] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count6] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count7] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count8] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count9] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count10] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count11] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count12] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count13] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count14] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count15] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count0] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count1] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count2] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count3] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count4] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count5] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count6] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count7] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count8] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count9] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count10] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count11] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count12] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count13] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count14] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count15] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count0] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count1] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count2] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count3] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count4] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count5] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count6] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count7] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count8] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count9] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count10] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count11] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count12] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count13] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count14] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count15] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count0] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count1] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count2] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count3] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count4] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count5] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count6] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count7] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count8] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count9] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count10] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count11] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count12] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count13] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count14] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count15] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count0] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count1] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count2] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count3] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count4] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count5] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count6] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count7] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count8] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count9] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count10] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count11] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count12] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count13] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count14] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count15] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count0] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count1] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count2] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count3] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count4] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count5] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count6] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count7] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count8] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count9] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count10] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count11] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count12] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count13] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count14] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count15] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count0-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count0-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count1-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count1-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count2-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count2-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count3-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count3-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count4-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count4-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count5-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count5-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count6-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count6-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count7-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count7-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count8-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count8-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count9-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count9-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count10-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count10-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count11-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count11-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count12-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count12-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count13-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count13-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count14-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count14-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count15-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count15-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count16-47] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count16-50] PASSED [ 34%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count17-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count17-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count18-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count18-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count19-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count19-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count20-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count20-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count21-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count21-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count22-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count22-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count23-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count23-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count24-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count24-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count25-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count25-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count26-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count26-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count27-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count27-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count28-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count28-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count29-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count29-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count30-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count30-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count31-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count31-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count32-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count32-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count33-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count33-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count34-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count34-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count35-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count35-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count36-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count36-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count37-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count37-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count38-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count38-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count39-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count39-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count40-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count40-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count41-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count41-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count42-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count42-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count43-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count43-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count44-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count44-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count45-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count45-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count46-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count46-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count47-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count47-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count0-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count0-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count1-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count1-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count2-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count2-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count3-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count3-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count4-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count4-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count5-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count5-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count6-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count6-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count7-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count7-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count8-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count8-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count9-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count9-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count10-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count10-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count11-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count11-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count12-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count12-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count13-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count13-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count14-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count14-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count15-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count15-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count16-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count16-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count17-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count17-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count18-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count18-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count19-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count19-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count20-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count20-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count21-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count21-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count22-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count22-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count23-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count23-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count24-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count24-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count25-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count25-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count26-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count26-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count27-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count27-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count28-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count28-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count29-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count29-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count30-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count30-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count31-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count31-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count32-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count32-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count33-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count33-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count34-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count34-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count35-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count35-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count36-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count36-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count37-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count37-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count38-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count38-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count39-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count39-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count40-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count40-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count41-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count41-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count42-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count42-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count43-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count43-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count44-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count44-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count45-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count45-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count46-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count46-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count47-47] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count47-50] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_int_check PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count0] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count1] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count2] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count3] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count4] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count5] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count6] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count7] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count8] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count9] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count10] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count11] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count12] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count13] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count14] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count15] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count0] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count1] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count2] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count3] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count4] PASSED [ 35%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count5] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count6] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count7] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count8] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count9] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count10] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count11] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count12] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count13] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count14] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[normal-count15] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count0] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count1] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count2] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count3] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count4] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count5] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count6] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count7] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count8] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count9] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count10] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count11] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count12] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count13] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count14] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[beta-count15] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count0] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count1] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count2] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count3] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count4] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count5] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count6] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count7] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count8] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count9] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count10] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count11] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count12] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count13] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count14] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[wilson-count15] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count0] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count1] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count2] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count3] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count4] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count5] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count6] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count7] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count8] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count9] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count10] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count11] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count12] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count13] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count14] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[jeffreys-count15] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count0] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count1] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count2] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count3] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count4] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count5] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count6] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count7] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count8] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count9] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count10] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count11] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count12] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count13] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count14] PASSED [ 36%] statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[binom_test-count15] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k0-0.01] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k0-0.05] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k1-0.01] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k1-0.05] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k2-0.01] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k2-0.05] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k3-0.01] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k3-0.05] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k4-0.01] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k4-0.05] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k5-0.01] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k5-0.05] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k6-0.01] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k6-0.05] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k7-0.01] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k7-0.05] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k8-0.01] PASSED [ 36%] statsmodels/stats/tests/test_qsturng.py::test_qstrung[k8-0.05] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_consistency[wald] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_consistency[score] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_consistency[exact-c] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_consistency[waldccv] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_consistency[sqrt-a] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_consistency[sqrt-v] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_consistency[midp-c] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_consistency[sqrt] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_r PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_tol_int[case0] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_tol_int[case1] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_tol_int[case2] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_tol_int[case3] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_tol_int[case4] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_test[wald] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_test[score] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_test[exact-c] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_test[midp-c] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_test[waldccv] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_test[sqrt-a] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_test[sqrt-v] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_test[sqrt] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_confint[wald] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_confint[score] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_confint[exact-c] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_confint[midp-c] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_confint[jeff] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_confint[waldccv] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_confint[sqrt-a] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_confint[sqrt-v] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_confint[sqrt] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_confint[sqrt-cent] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompar1samp::test_confint[sqrt-centcc] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_diff_consistency[wald] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_diff_consistency[score] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_diff_consistency[waldccv] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_ratio_consistency[wald-log] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_ratio_consistency[score-log] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_diff_ratio_consistency[wald] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_diff_ratio_consistency[score] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_diff_ratio_consistency[etest] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_rate_poisson_diff_ratio_consistency[etest-wald] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_twosample_poisson PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_twosample_poisson_diff[case0] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_twosample_poisson_diff[case1] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_twosample_poisson_diff[case2] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_twosample_poisson_diff[case3] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_twosample_poisson_r PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_confint_poisson_2indep PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_tost_poisson PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case0] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case1] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case2] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case3] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case4] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case5] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case6] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case7] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case8] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case9] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case10] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case11] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case12] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case13] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case14] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case15] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case16] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case17] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case18] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case19] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::test_alternative[case20] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test[ratio-wald] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test[ratio-score] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test[ratio-score-log] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test[ratio-wald-log] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test[ratio-exact-cond] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test[ratio-cond-midp] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test[ratio-sqrt] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test[ratio-etest-score] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test[ratio-etest-wald] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test[diff-wald] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test[diff-score] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test[diff-waldccv] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test[diff-etest-score] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test[diff-etest-wald] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_confint[ratio-waldcc] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_confint[ratio-score] PASSED [ 36%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_confint[ratio-score-log] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_confint[ratio-wald-log] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_confint[ratio-sqrtcc] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_confint[ratio-mover] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_confint[diff-wald] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_confint[diff-score] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_confint[diff-waldccv] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_confint[diff-mover] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test_vectorized[ratio-wald] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test_vectorized[ratio-score] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test_vectorized[ratio-score-log] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test_vectorized[ratio-wald-log] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test_vectorized[ratio-exact-cond] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test_vectorized[ratio-cond-midp] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test_vectorized[ratio-sqrt] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test_vectorized[ratio-etest-score] SKIPPED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test_vectorized[ratio-etest-wald] SKIPPED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test_vectorized[diff-wald] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test_vectorized[diff-score] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test_vectorized[diff-waldccv] PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test_vectorized[diff-etest-score] SKIPPED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::TestMethodsCompare2indep::test_test_vectorized[diff-etest-wald] SKIPPED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::test_y_grid_regression PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::test_invalid_y_grid PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::test_poisson_power_2ratio PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::test_power_poisson_equal PASSED [ 37%] statsmodels/stats/tests/test_rates_poisson.py::test_power_negbin PASSED [ 37%] statsmodels/stats/tests/test_regularized_covariance.py::test_calc_nodewise_row PASSED [ 37%] statsmodels/stats/tests/test_regularized_covariance.py::test_calc_nodewise_weight PASSED [ 37%] statsmodels/stats/tests/test_regularized_covariance.py::test_calc_approx_inv_cov PASSED [ 37%] statsmodels/stats/tests/test_regularized_covariance.py::test_fit PASSED [ 37%] statsmodels/stats/tests/test_robust_compare.py::Test_Trim::test_trimboth PASSED [ 37%] statsmodels/stats/tests/test_robust_compare.py::Test_Trim::test_trim_mean PASSED [ 37%] statsmodels/stats/tests/test_robust_compare.py::TestTrimmedR1::test_basic PASSED [ 37%] statsmodels/stats/tests/test_robust_compare.py::TestTrimmedR1::test_inference PASSED [ 37%] statsmodels/stats/tests/test_robust_compare.py::TestTrimmedR1::test_other PASSED [ 37%] statsmodels/stats/tests/test_robust_compare.py::TestTrimmedR1::test_vectorized[0] PASSED [ 37%] statsmodels/stats/tests/test_robust_compare.py::TestTrimmedR1::test_vectorized[1] PASSED [ 37%] statsmodels/stats/tests/test_robust_compare.py::TestTrimmedRAnova::test_oneway PASSED [ 37%] statsmodels/stats/tests/test_sandwich.py::test_cov_cluster_2groups PASSED [ 37%] statsmodels/stats/tests/test_sandwich.py::test_hac_simple PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::test_durbin_watson PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::test_omni_normtest PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::test_omni_normtest_axis PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::test_jarque_bera PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::test_shapiro PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::test_adnorm PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::test_durbin_watson_pandas PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_medcouple_no_axis PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_medcouple_1d PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_medcouple_symmetric PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_medcouple_nonzero PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_medcouple_int PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_medcouple_symmetry PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_medcouple_ties PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_durbin_watson PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_durbin_watson_2d PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_durbin_watson_3d PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_robust_skewness_1d PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_robust_skewness_1d_2d PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_robust_skewness_symmetric PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_robust_skewness_3d PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_robust_skewness_4 PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_robust_kurtosis_1d_2d PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_robust_kurtosis PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_robust_kurtosis_3d PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_robust_kurtosis_excess_false PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_robust_kurtosis_ab PASSED [ 37%] statsmodels/stats/tests/test_statstools.py::TestStattools::test_robust_kurtosis_dg PASSED [ 37%] statsmodels/stats/tests/test_tabledist.py::test_tabledist PASSED [ 37%] statsmodels/stats/tests/test_tost.py::TestTostp1::test_pval PASSED [ 37%] statsmodels/stats/tests/test_tost.py::TestTostp1::test_special PASSED [ 37%] statsmodels/stats/tests/test_tost.py::TestTostp2::test_pval PASSED [ 37%] statsmodels/stats/tests/test_tost.py::TestTosti1::test_pval PASSED [ 37%] statsmodels/stats/tests/test_tost.py::TestTosti2::test_pval PASSED [ 37%] statsmodels/stats/tests/test_tost.py::TestTostip1::test_pval PASSED [ 37%] statsmodels/stats/tests/test_tost.py::TestTostip2::test_pval PASSED [ 37%] statsmodels/stats/tests/test_tost.py::test_tost_log PASSED [ 37%] statsmodels/stats/tests/test_tost.py::test_tost_asym PASSED [ 37%] statsmodels/stats/tests/test_tost.py::test_ttest PASSED [ 37%] statsmodels/stats/tests/test_tost.py::test_tost_transform_paired XFAIL [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1::test_mean PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1::test_sum PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1::test_var PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1::test_std PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1::test_sem PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1::test_quantiles PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1t::test_mean PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1t::test_sum PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1t::test_var PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1t::test_std PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1t::test_sem PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1t::test_quantiles PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1n::test_mean PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1n::test_sum PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1n::test_var PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1n::test_std PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1n::test_sem PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim1n::test_quantiles PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim2::test_mean PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim2::test_sum PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim2::test_var PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim2::test_std PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim2::test_sem PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestSim2::test_quantiles PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats::test_weightstats_1 PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats::test_weightstats_2 PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats::test_weightstats_3 PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats::test_weightstats_ddof_tests PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats::test_comparemeans_convenient_interface PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats::test_comparemeans_convenient_interface_1d PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats1d_ddof::test_basic PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats1d_ddof::test_ttest PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats1d_ddof::test_ttest_2sample PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats1d_ddof::test_confint_mean PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats2d::test_basic PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats2d::test_ttest PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats2d::test_ttest_2sample PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats2d::test_confint_mean PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats2d::test_corr PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats2d_ddof::test_basic PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats2d_ddof::test_ttest PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats2d_ddof::test_ttest_2sample PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats2d_ddof::test_confint_mean PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats2d_ddof::test_corr PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats2d_nobs::test_basic PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats2d_nobs::test_ttest PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats2d_nobs::test_ttest_2sample PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats2d_nobs::test_confint_mean PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestWeightstats2d_nobs::test_corr PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::test_ttest_ind_with_uneq_var PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::test_ztest_ztost PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::TestZTest::test PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::test_weightstats_len_1 PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::test_weightstats_2d_w1 PASSED [ 37%] statsmodels/stats/tests/test_weightstats.py::test_weightstats_2d_w2 PASSED [ 37%] statsmodels/tests/test_package.py::test_lazy_imports PASSED [ 37%] statsmodels/tests/test_package.py::test_docstring_optimization_compat PASSED [ 37%] statsmodels/tests/test_x13.py::test_make_var_names PASSED [ 37%] statsmodels/tools/tests/test_catadd.py::test_add_indep PASSED [ 37%] statsmodels/tools/tests/test_data.py::test_missing_data_pandas PASSED [ 37%] statsmodels/tools/tests/test_data.py::test_dataframe PASSED [ 37%] statsmodels/tools/tests/test_data.py::test_patsy_577 PASSED [ 37%] statsmodels/tools/tests/test_decorators.py::test_cache_readonly PASSED [ 37%] statsmodels/tools/tests/test_decorators.py::test_deprecated_alias[test message-None-FutureWarning] PASSED [ 37%] statsmodels/tools/tests/test_decorators.py::test_deprecated_alias[test message-None-UserWarning] PASSED [ 37%] statsmodels/tools/tests/test_decorators.py::test_deprecated_alias[test message-0.11-FutureWarning] PASSED [ 37%] statsmodels/tools/tests/test_decorators.py::test_deprecated_alias[test message-0.11-UserWarning] PASSED [ 37%] statsmodels/tools/tests/test_decorators.py::test_deprecated_alias[None-None-FutureWarning] PASSED [ 37%] statsmodels/tools/tests/test_decorators.py::test_deprecated_alias[None-None-UserWarning] PASSED [ 37%] statsmodels/tools/tests/test_decorators.py::test_deprecated_alias[None-0.11-FutureWarning] PASSED [ 37%] statsmodels/tools/tests/test_decorators.py::test_deprecated_alias[None-0.11-UserWarning] PASSED [ 37%] statsmodels/tools/tests/test_docstring.py::test_remove_parameter PASSED [ 37%] statsmodels/tools/tests/test_docstring.py::test_insert_parameters PASSED [ 37%] statsmodels/tools/tests/test_docstring.py::test_set_unknown PASSED [ 37%] statsmodels/tools/tests/test_docstring.py::test_replace_block PASSED [ 37%] statsmodels/tools/tests/test_docstring.py::test_repeat PASSED [ 37%] statsmodels/tools/tests/test_docstring.py::test_bad PASSED [ 37%] statsmodels/tools/tests/test_docstring.py::test_empty_ds PASSED [ 37%] statsmodels/tools/tests/test_docstring.py::test_yield_return PASSED [ 37%] statsmodels/tools/tests/test_docstring.py::test_multiple_sig PASSED [ 37%] statsmodels/tools/tests/test_eval_measures.py::test_eval_measures PASSED [ 37%] statsmodels/tools/tests/test_eval_measures.py::test_ic_equivalence[aic-aic_sigma] PASSED [ 37%] 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statsmodels/tools/tests/test_grouputils.py::TestMultiIndexGrouping::test_transform_slices PASSED [ 37%] statsmodels/tools/tests/test_grouputils.py::TestMultiIndexGrouping::test_dummies_groups PASSED [ 37%] statsmodels/tools/tests/test_grouputils.py::TestMultiIndexGrouping::test_dummy_sparse PASSED [ 37%] statsmodels/tools/tests/test_grouputils.py::TestIndexGrouping::test_reindex PASSED [ 37%] statsmodels/tools/tests/test_grouputils.py::TestIndexGrouping::test_count_categories PASSED [ 37%] statsmodels/tools/tests/test_grouputils.py::TestIndexGrouping::test_sort PASSED [ 37%] statsmodels/tools/tests/test_grouputils.py::TestIndexGrouping::test_transform_dataframe PASSED [ 37%] statsmodels/tools/tests/test_grouputils.py::TestIndexGrouping::test_transform_array PASSED [ 38%] statsmodels/tools/tests/test_grouputils.py::TestIndexGrouping::test_transform_slices PASSED [ 38%] statsmodels/tools/tests/test_grouputils.py::TestIndexGrouping::test_dummies_groups PASSED [ 38%] statsmodels/tools/tests/test_grouputils.py::TestIndexGrouping::test_dummy_sparse PASSED [ 38%] statsmodels/tools/tests/test_grouputils.py::test_init_api PASSED [ 38%] statsmodels/tools/tests/test_grouputils.py::test_combine_indices PASSED [ 38%] statsmodels/tools/tests/test_grouputils.py::test_group_sums PASSED [ 38%] statsmodels/tools/tests/test_grouputils.py::test_group_class PASSED [ 38%] statsmodels/tools/tests/test_grouputils.py::test_dummy_sparse PASSED [ 38%] statsmodels/tools/tests/test_linalg.py::test_stationary_solve_1d PASSED [ 38%] statsmodels/tools/tests/test_linalg.py::test_stationary_solve_2d PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestGradMNLogit::test_score PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestGradMNLogit::test_hess PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestGradLogit::test_score PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestGradLogit::test_hess PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestDerivativeFun::test_grad_fun1_fd PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestDerivativeFun::test_grad_fun1_fdc PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestDerivativeFun::test_grad_fun1_cs PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestDerivativeFun::test_hess_fun1_fd PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestDerivativeFun::test_hess_fun1_cs PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestDerivativeFun2::test_grad_fun1_fd PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestDerivativeFun2::test_grad_fun1_fdc PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestDerivativeFun2::test_grad_fun1_cs PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestDerivativeFun2::test_hess_fun1_fd PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestDerivativeFun2::test_hess_fun1_cs PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestDerivativeFun1::test_grad_fun1_fd PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestDerivativeFun1::test_grad_fun1_fdc PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestDerivativeFun1::test_grad_fun1_cs PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestDerivativeFun1::test_hess_fun1_fd PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::TestDerivativeFun1::test_hess_fun1_cs PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::test_dtypes PASSED [ 38%] statsmodels/tools/tests/test_numdiff.py::test_vectorized PASSED [ 38%] statsmodels/tools/tests/test_parallel.py::test_parallel PASSED [ 38%] statsmodels/tools/tests/test_rootfinding.py::test_brentq_expanding PASSED [ 38%] statsmodels/tools/tests/test_sequences.py::test_discrepancy PASSED [ 38%] statsmodels/tools/tests/test_sequences.py::test_van_der_corput PASSED [ 38%] statsmodels/tools/tests/test_sequences.py::test_primes PASSED [ 38%] statsmodels/tools/tests/test_sequences.py::test_halton PASSED [ 38%] statsmodels/tools/tests/test_testing.py::test_bad_table PASSED [ 38%] statsmodels/tools/tests/test_testing.py::test_holder PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestTools::test_add_constant_list PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestTools::test_add_constant_1d PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestTools::test_add_constant_has_constant1d PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestTools::test_add_constant_has_constant2d PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestTools::test_add_constant_series PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestTools::test_add_constant_dataframe PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestTools::test_add_constant_zeros PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestTools::test_recipr PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestTools::test_recipr0 PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestTools::test_extendedpinv PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestTools::test_extendedpinv_singular PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestTools::test_fullrank PASSED [ 38%] statsmodels/tools/tests/test_tools.py::test_estimable PASSED [ 38%] statsmodels/tools/tests/test_tools.py::test_pandas_const_series PASSED [ 38%] statsmodels/tools/tests/test_tools.py::test_pandas_const_series_prepend PASSED [ 38%] statsmodels/tools/tests/test_tools.py::test_pandas_const_df PASSED [ 38%] statsmodels/tools/tests/test_tools.py::test_pandas_const_df_prepend PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestNanDot::test_11 PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestNanDot::test_12 PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestNanDot::test_13 PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestNanDot::test_14 PASSED [ 38%] statsmodels/tools/tests/test_tools.py::TestNanDot::test_41 PASSED [ 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statsmodels/tools/tests/test_web.py::TestWeb::test_errors PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::TestArrayLike::test_1d[True] PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::TestArrayLike::test_1d[False] PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::TestArrayLike::test_2d[True] PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::TestArrayLike::test_2d[False] PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::TestArrayLike::test_3d PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::TestArrayLike::test_right_squeeze_and_pad PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::TestArrayLike::test_contiguous PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::TestArrayLike::test_dtype PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::TestArrayLike::test_dot[True] XFAIL [ 38%] statsmodels/tools/validation/tests/test_validation.py::TestArrayLike::test_dot[False] XFAIL [ 38%] statsmodels/tools/validation/tests/test_validation.py::TestArrayLike::test_slice[True] PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::TestArrayLike::test_slice[False] PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::test_right_squeeze PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::test_wrap_pandas[True] PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::test_wrap_pandas[False] PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::test_wrap_pandas_append PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::test_wrap_pandas_append_non_string PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::test_optional_dict_like[dict] PASSED [ 38%] statsmodels/tools/validation/tests/test_validation.py::test_optional_dict_like[OrderedDict] PASSED [ 38%] 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statsmodels/tsa/ardl/tests/test_ardl.py::test_ardl_select_order[3-2-n-True-2-False-True-None] PASSED [ 44%] statsmodels/tsa/ardl/tests/test_ardl.py::test_ardl_select_order[3-2-n-True-2-True-False-None] PASSED [ 44%] statsmodels/tsa/ardl/tests/test_ardl.py::test_ardl_select_order[3-2-n-True-2-True-True-None] PASSED [ 44%] statsmodels/tsa/ardl/tests/test_ardl.py::test_ardl_select_order[3-2-n-True-None-False-False-None] PASSED [ 44%] statsmodels/tsa/ardl/tests/test_ardl.py::test_ardl_select_order[3-2-n-True-None-False-True-None] PASSED [ 44%] statsmodels/tsa/ardl/tests/test_ardl.py::test_ardl_select_order[3-2-n-True-None-True-False-None] PASSED [ 44%] statsmodels/tsa/ardl/tests/test_ardl.py::test_ardl_select_order[3-2-n-True-None-True-True-None] PASSED [ 44%] statsmodels/tsa/ardl/tests/test_ardl.py::test_ardl_select_order[0-maxorder1-ct-False-2-False-False-None] PASSED [ 44%] statsmodels/tsa/ardl/tests/test_ardl.py::test_ardl_select_order[0-maxorder1-ct-False-2-False-True-None] PASSED [ 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PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-uecm_order1-ct-True-None-False-True] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-uecm_order1-ct-True-None-True-False] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_formula[3-uecm_order1-ct-True-None-False] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-uecm_order1-ct-True-None-True-True] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_formula[3-uecm_order1-ct-True-None-True] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-uecm_order1-c-False-2-False-False] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-uecm_order1-c-False-2-False-True] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-uecm_order1-c-False-2-True-False] PASSED [ 45%] 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PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-uecm_order1-c-True-2-False-False] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-uecm_order1-c-True-2-False-True] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-uecm_order1-c-True-2-True-False] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_formula[3-uecm_order1-c-True-2-False] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-uecm_order1-c-True-2-True-True] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_formula[3-uecm_order1-c-True-2-True] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-uecm_order1-c-True-None-False-False] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-uecm_order1-c-True-None-False-True] PASSED [ 45%] 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statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-2-ct-False-None-False-True] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-2-ct-False-None-True-False] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_formula[3-2-ct-False-None-False] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-2-ct-False-None-True-True] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_formula[3-2-ct-False-None-True] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-2-ct-True-2-False-False] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-2-ct-True-2-False-True] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_init[3-2-ct-True-2-True-False] PASSED [ 45%] statsmodels/tsa/ardl/tests/test_ardl.py::test_uecm_model_formula[3-2-ct-True-2-False] PASSED [ 45%] 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[ 48%] statsmodels/tsa/base/tests/test_tsa_indexes.py::test_get_index_loc_quarterly PASSED [ 48%] statsmodels/tsa/filters/tests/test_filters.py::test_bking1d PASSED [ 48%] statsmodels/tsa/filters/tests/test_filters.py::test_bking2d PASSED [ 48%] statsmodels/tsa/filters/tests/test_filters.py::test_hpfilter PASSED [ 48%] statsmodels/tsa/filters/tests/test_filters.py::test_cfitz_filter PASSED [ 48%] statsmodels/tsa/filters/tests/test_filters.py::test_bking_pandas PASSED [ 48%] statsmodels/tsa/filters/tests/test_filters.py::test_cfitz_pandas PASSED [ 48%] statsmodels/tsa/filters/tests/test_filters.py::test_hpfilter_pandas PASSED [ 48%] statsmodels/tsa/filters/tests/test_filters.py::TestFilters::test_convolution PASSED [ 48%] statsmodels/tsa/filters/tests/test_filters.py::TestFilters::test_convolution2d PASSED [ 48%] statsmodels/tsa/filters/tests/test_filters.py::TestFilters::test_recursive PASSED [ 48%] statsmodels/tsa/filters/tests/test_filters.py::TestFilters::test_pandas PASSED [ 48%] 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statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-True-False-False-False-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-True-False-False-False-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-True-True-True-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-True-True-True-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-True-True-True-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-True-True-False-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: 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statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-True-False-False-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-False-True-True-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-False-True-True-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-False-True-True-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-False-True-False-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-False-True-False-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-False-True-False-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-False-False-True-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-False-False-True-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-False-False-True-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-False-False-False-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-False-False-False-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: True-multiplicative-False-False-False-False-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-True-True-True-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-True-True-True-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-True-True-True-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-True-True-False-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-True-True-False-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-True-True-False-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-True-False-True-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-True-False-True-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-True-False-True-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-True-False-False-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-True-False-False-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-True-False-False-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-False-True-True-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-False-True-True-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-False-True-True-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-False-True-False-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-False-True-False-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-False-True-False-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-False-False-True-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-False-False-True-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-False-False-True-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-False-False-False-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-False-False-False-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-True-False-False-False-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-True-True-True-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-True-True-True-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-True-True-True-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-True-True-False-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-True-True-False-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-True-True-False-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-True-False-True-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-True-False-True-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-True-False-True-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-True-False-False-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-True-False-False-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-True-False-False-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-False-True-True-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-False-True-True-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-False-True-True-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-False-True-False-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-False-True-False-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-False-True-False-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-False-False-True-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-False-False-True-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-False-False-True-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-False-False-False-None] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-False-False-False-4] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-auto-False-False-False-False-12] PASSED [ 49%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-True-True-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-True-True-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-True-True-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-True-True-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-True-True-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-True-True-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-True-False-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-True-False-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-True-False-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-True-False-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-True-False-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-True-False-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-False-True-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-False-True-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-False-True-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-False-True-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-False-True-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-False-True-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-False-False-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-False-False-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-False-False-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-False-False-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-False-False-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-True-False-False-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-True-True-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-True-True-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-True-True-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-True-True-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-True-True-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-True-True-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-True-False-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-True-False-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-True-False-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-True-False-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-True-False-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-True-False-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-False-True-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-False-True-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-False-True-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-False-True-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-False-True-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-False-True-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-False-False-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-False-False-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-False-False-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-False-False-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-False-False-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-additive-False-False-False-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-True-True-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-True-True-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-True-True-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-True-True-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-True-True-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-True-True-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-True-False-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-True-False-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-True-False-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-True-False-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-True-False-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-True-False-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-False-True-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-False-True-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-False-True-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-False-True-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-False-True-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-False-True-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-False-False-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-False-False-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-False-False-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-False-False-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-False-False-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-True-False-False-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-True-True-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-True-True-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-True-True-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-True-True-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-True-True-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-True-True-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-True-False-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-True-False-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-True-False-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-True-False-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-True-False-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-True-False-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-False-True-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-False-True-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-False-True-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-False-True-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-False-True-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-False-True-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-False-False-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-False-False-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-False-False-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-False-False-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-False-False-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: array, exponential: False-multiplicative-False-False-False-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-True-True-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-True-True-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-True-True-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-True-True-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-True-True-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-True-True-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-True-False-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-True-False-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-True-False-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-True-False-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-True-False-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-True-False-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-False-True-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-False-True-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-False-True-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-False-True-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-False-True-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-False-True-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-False-False-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-False-False-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-False-False-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-False-False-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-False-False-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-True-False-False-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-True-True-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-True-True-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-True-True-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-True-True-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-True-True-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-True-True-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-True-False-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-True-False-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-True-False-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-True-False-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-True-False-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-True-False-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-False-True-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-False-True-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-False-True-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-False-True-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-False-True-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-False-True-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-False-False-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-False-False-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-False-False-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-False-False-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-False-False-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-auto-False-False-False-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-True-True-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-True-True-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-True-True-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-True-True-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-True-True-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-True-True-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-True-False-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-True-False-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-True-False-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-True-False-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-True-False-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-True-False-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-False-True-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-False-True-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-False-True-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-False-True-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-False-True-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-False-True-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-False-False-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-False-False-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-False-False-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-False-False-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-False-False-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-True-False-False-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-True-True-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-True-True-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-True-True-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-True-True-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-True-True-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-True-True-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-True-False-True-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-True-False-True-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-True-False-True-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-True-False-False-None] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-True-False-False-4] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-True-False-False-12] PASSED [ 50%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-False-True-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-False-True-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-False-True-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-False-True-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-False-True-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-False-True-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-False-False-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-False-False-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-False-False-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-False-False-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-False-False-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-additive-False-False-False-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-True-True-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-True-True-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-True-True-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-True-True-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-True-True-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-True-True-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-True-False-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-True-False-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-True-False-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-True-False-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-True-False-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-True-False-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-False-True-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-False-True-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-False-True-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-False-True-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-False-True-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-False-True-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-False-False-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-False-False-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-False-False-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-False-False-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-False-False-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-True-False-False-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-True-True-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-True-True-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-True-True-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-True-True-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-True-True-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-True-True-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-True-False-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-True-False-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-True-False-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-True-False-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-True-False-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-True-False-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-False-True-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-False-True-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-False-True-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-False-True-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-False-True-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-False-True-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-False-False-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-False-False-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-False-False-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-False-False-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-False-False-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: True-multiplicative-False-False-False-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-True-True-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-True-True-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-True-True-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-True-True-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-True-True-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-True-True-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-True-False-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-True-False-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-True-False-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-True-False-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-True-False-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-True-False-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-False-True-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-False-True-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-False-True-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-False-True-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-False-True-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-False-True-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-False-False-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-False-False-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-False-False-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-False-False-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-False-False-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-True-False-False-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-True-True-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-True-True-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-True-True-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-True-True-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-True-True-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-True-True-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-True-False-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-True-False-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-True-False-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-True-False-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-True-False-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-True-False-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-False-True-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-False-True-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-False-True-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-False-True-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-False-True-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-False-True-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-False-False-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-False-False-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-False-False-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-False-False-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-False-False-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-auto-False-False-False-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-True-True-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-True-True-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-True-True-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-True-True-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-True-True-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-True-True-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-True-False-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-True-False-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-True-False-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-True-False-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-True-False-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-True-False-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-False-True-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-False-True-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-False-True-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-False-True-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-False-True-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-False-True-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-False-False-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-False-False-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-False-False-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-False-False-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-False-False-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-True-False-False-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-True-True-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-True-True-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-True-True-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-True-True-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-True-True-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-True-True-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-True-False-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-True-False-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-True-False-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-True-False-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-True-False-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-True-False-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-False-True-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-False-True-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-False-True-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-False-True-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-False-True-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-False-True-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-False-False-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-False-False-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-False-False-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-False-False-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-False-False-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-additive-False-False-False-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-True-True-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-True-True-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-True-True-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-True-True-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-True-True-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-True-True-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-True-False-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-True-False-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-True-False-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-True-False-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-True-False-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-True-False-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-False-True-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-False-True-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-False-True-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-False-True-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-False-True-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-False-True-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-False-False-True-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-False-False-True-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-False-False-True-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-False-False-False-None] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-False-False-False-4] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-True-False-False-False-12] PASSED [ 51%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-True-True-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-True-True-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-True-True-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-True-True-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-True-True-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-True-True-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-True-False-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-True-False-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-True-False-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-True-False-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-True-False-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-True-False-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-False-True-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-False-True-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-False-True-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-False-True-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-False-True-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-False-True-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-False-False-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-False-False-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-False-False-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-False-False-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-False-False-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: dataframe, exponential: False-multiplicative-False-False-False-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-True-True-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: 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statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-True-False-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-True-False-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-True-False-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-False-True-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-False-True-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-False-True-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-False-True-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-False-True-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-False-True-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-False-False-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-False-False-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-False-False-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-False-False-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-False-False-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-True-False-False-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-True-True-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-True-True-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-True-True-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-True-True-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-True-True-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-True-True-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-True-False-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-True-False-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-True-False-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-True-False-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-True-False-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-True-False-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-False-True-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-False-True-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-False-True-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-False-True-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-False-True-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-False-True-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-False-False-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-False-False-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-False-False-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-False-False-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-False-False-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-auto-False-False-False-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-True-True-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-True-True-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-True-True-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-True-True-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-True-True-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-True-True-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-True-False-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-True-False-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-True-False-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-True-False-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-True-False-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-True-False-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-False-True-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-False-True-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-False-True-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-False-True-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-False-True-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-False-True-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-False-False-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-False-False-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-False-False-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-False-False-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-False-False-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-True-False-False-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-True-True-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-True-True-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-True-True-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-True-True-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-True-True-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-True-True-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-True-False-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-True-False-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-True-False-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-True-False-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-True-False-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-True-False-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-False-True-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-False-True-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-False-True-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-False-True-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-False-True-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-False-True-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-False-False-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-False-False-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-False-False-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-False-False-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-False-False-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-additive-False-False-False-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-True-True-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-True-True-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-True-True-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-True-True-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-True-True-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-True-True-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-True-False-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-True-False-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-True-False-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-True-False-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-True-False-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-True-False-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-False-True-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-False-True-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-False-True-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-False-True-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-False-True-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-False-True-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-False-False-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-False-False-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-False-False-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-False-False-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-False-False-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-True-False-False-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-True-True-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-True-True-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-True-True-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-True-True-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-True-True-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-True-True-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-True-False-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-True-False-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-True-False-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-True-False-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-True-False-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-True-False-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-False-True-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-False-True-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-False-True-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-False-True-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-False-True-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-False-True-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-False-False-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-False-False-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-False-False-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-False-False-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-False-False-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: True-multiplicative-False-False-False-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-True-True-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-True-True-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-True-True-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-True-True-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-True-True-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-True-True-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-True-False-True-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-True-False-True-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-True-False-True-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-True-False-False-None] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-True-False-False-4] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-True-False-False-12] PASSED [ 52%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-False-True-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-False-True-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-False-True-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-False-True-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-False-True-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-False-True-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-False-False-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-False-False-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-False-False-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-False-False-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-False-False-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-True-False-False-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-True-True-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-True-True-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-True-True-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-True-True-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-True-True-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-True-True-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-True-False-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-True-False-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-True-False-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-True-False-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-True-False-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-True-False-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-False-True-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-False-True-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-False-True-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-False-True-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-False-True-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-False-True-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-False-False-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-False-False-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-False-False-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-False-False-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-False-False-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-auto-False-False-False-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-True-True-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-True-True-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-True-True-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-True-True-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-True-True-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-True-True-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-True-False-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-True-False-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-True-False-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-True-False-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-True-False-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-True-False-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-False-True-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-False-True-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-False-True-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-False-True-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-False-True-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-False-True-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-False-False-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-False-False-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-False-False-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-False-False-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-False-False-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-True-False-False-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-True-True-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-True-True-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-True-True-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-True-True-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-True-True-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-True-True-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-True-False-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-True-False-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-True-False-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-True-False-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-True-False-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-True-False-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-False-True-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-False-True-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-False-True-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-False-True-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-False-True-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-False-True-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-False-False-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-False-False-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-False-False-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-False-False-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-False-False-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-additive-False-False-False-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-True-True-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-True-True-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-True-True-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-True-True-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-True-True-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-True-True-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-True-False-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-True-False-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-True-False-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-True-False-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-True-False-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-True-False-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-False-True-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-False-True-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-False-True-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-False-True-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-False-True-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-False-True-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-False-False-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-False-False-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-False-False-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-False-False-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-False-False-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-True-False-False-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-True-True-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-True-True-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-True-True-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-True-True-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-True-True-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-True-True-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-True-False-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-True-False-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-True-False-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-True-False-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-True-False-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-True-False-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-False-True-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-False-True-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-False-True-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-False-True-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-False-True-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-False-True-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-False-False-True-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-False-False-True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-False-False-True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-False-False-False-None] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-False-False-False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_smoke[type: series, exponential: False-multiplicative-False-False-False-False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_alt_index[datetime] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_alt_index[period] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_alt_index[range] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_alt_index[nofreq] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_no_freq PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_errors[type: array, exponential: True] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_errors[type: array, exponential: False] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_errors[type: dataframe, exponential: True] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_errors[type: dataframe, exponential: False] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_errors[type: series, exponential: True] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_errors[type: series, exponential: False] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_pi_width PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_seasonal_alignment[type: array, exponential: True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_seasonal_alignment[type: array, exponential: True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_seasonal_alignment[type: array, exponential: False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_seasonal_alignment[type: array, exponential: False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_seasonal_alignment[type: dataframe, exponential: True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_seasonal_alignment[type: dataframe, exponential: True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_seasonal_alignment[type: dataframe, exponential: False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_seasonal_alignment[type: dataframe, exponential: False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_seasonal_alignment[type: series, exponential: True-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_seasonal_alignment[type: series, exponential: True-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_seasonal_alignment[type: series, exponential: False-4] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_forecast_seasonal_alignment[type: series, exponential: False-12] PASSED [ 53%] statsmodels/tsa/forecasting/tests/test_theta.py::test_auto PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::TestHoltWinters::test_predict PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::TestHoltWinters::test_ndarray PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::TestHoltWinters::test_forecast XFAIL [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::TestHoltWinters::test_simple_exp_smoothing PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::TestHoltWinters::test_holt PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::TestHoltWinters::test_holt_damp_fit PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::TestHoltWinters::test_holt_damp_r PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::TestHoltWinters::test_hw_seasonal PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::TestHoltWinters::test_hw_seasonal_add_mul PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::TestHoltWinters::test_hw_seasonal_buggy PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_negative_multipliative[trend_seasonal0] PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_negative_multipliative[trend_seasonal1] PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_negative_multipliative[trend_seasonal2] PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_dampen_no_trend[add] PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_dampen_no_trend[mul] PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_dampen_no_trend[None] PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_invalid_seasonal[add] PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_invalid_seasonal[mul] PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_2d_data PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_infer_freq PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_start_params[mul-mul] PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_no_params_to_optimize PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_invalid_start_param_length PASSED [ 53%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_basin_hopping PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_debiased PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_float_boxcox[add-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_float_boxcox[add-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_float_boxcox[add-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_float_boxcox[mul-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_float_boxcox[mul-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_float_boxcox[mul-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_float_boxcox[None-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_float_boxcox[None-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_float_boxcox[None-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_equivalence_cython_python[add-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_equivalence_cython_python[add-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_equivalence_cython_python[add-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_equivalence_cython_python[mul-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_equivalence_cython_python[mul-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_equivalence_cython_python[mul-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_equivalence_cython_python[None-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_equivalence_cython_python[None-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_equivalence_cython_python[None-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_direct_holt_add PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_integer_array PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_damping_trend_zero PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_different_inputs PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-True-add-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-True-add-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-True-add-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-True-mul-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-True-mul-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-True-mul-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-True-None-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-True-None-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-True-None-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-False-add-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-False-add-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-False-add-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-False-mul-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-False-mul-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-False-mul-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-False-None-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-False-None-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[add-False-None-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-True-add-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-True-add-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-True-add-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-True-mul-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-True-mul-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-True-mul-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-True-None-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-True-None-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-True-None-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-False-add-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-False-add-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-False-add-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-False-mul-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-False-mul-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-False-mul-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-False-None-add] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-False-None-mul] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_expected_r[mul-False-None-None] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_keywords PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_simulate_boxcox PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_forecast_index[10] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_forecast_index[100] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_forecast_index[1000] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_forecast_index[2000] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_error_dampen PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_error_boxcox PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_error_initialization PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_alternative_minimizers[least_squares] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_alternative_minimizers[basinhopping] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_alternative_minimizers[L-BFGS-B] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_alternative_minimizers[TNC] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_alternative_minimizers[SLSQP] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_alternative_minimizers[Powell] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_alternative_minimizers[trust-constr] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_minimizer_kwargs_error PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_to_restricted_equiv[params0] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_to_restricted_equiv[params1] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_to_restricted_equiv[params2] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_restricted_round_tip[params0] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_restricted_round_tip[params1] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_restricted_round_tip[params2] PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_bad_bounds PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_valid_bounds PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_fixed_basic PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_fixed_errors PASSED [ 54%] statsmodels/tsa/holtwinters/tests/test_holtwinters.py::test_brute[add-add] PASSED [ 54%] 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statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR::test_params PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR::test_results PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR::test_plot_coefficients_of_determination PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR::test_no_enforce PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR::test_mle PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR::test_loglike PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR::test_aic PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR::test_bic PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR::test_bse_approx PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR::test_predict PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR::test_dynamic_predict PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR_autocorrelated_errors::test_params PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR_autocorrelated_errors::test_results PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR_autocorrelated_errors::test_plot_coefficients_of_determination PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR_autocorrelated_errors::test_no_enforce PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR_autocorrelated_errors::test_loglike PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR_autocorrelated_errors::test_aic PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor.py::TestSUR_autocorrelated_errors::test_bic PASSED [ 58%] 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statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_date_indexes[ME-QE] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_date_indexes[ME-QE-DEC] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_date_indexes[ME-QE-JAN] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_date_indexes[ME-QS] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_date_indexes[ME-QS-DEC] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_date_indexes[ME-QS-APR] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_date_indexes[MS-QE] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_date_indexes[MS-QE-DEC] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_date_indexes[MS-QE-JAN] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_date_indexes[MS-QS] 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PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_standardized_MQ[False] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[111] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[112] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[11F] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[221] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[222] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[22F] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[block_111] PASSED [ 58%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[block_112] PASSED [ 58%] 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statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[3-factor_orders3-1-True] PASSED [ 59%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[1-1-1-False] PASSED [ 59%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[3-1-1-False] PASSED [ 59%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[1-6-1-False] PASSED [ 59%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[3-factor_orders7-1-False] PASSED [ 59%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[k_factors8-factor_orders8-1-True] PASSED [ 59%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[k_factors9-factor_orders9-1-False] PASSED [ 59%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[k_factors10-factor_orders10-factor_multiplicities10-True] PASSED [ 59%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[k_factors11-factor_orders11-factor_multiplicities11-False] PASSED [ 59%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_news[news_112] PASSED [ 59%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_news[news_222] PASSED [ 59%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_news[news_block_112] PASSED [ 59%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_news[news_block_222] PASSED [ 59%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py::test_k_factor1 SKIPPED [ 59%] statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py::test_em_nonstationary 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statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::test_common_level_analytic PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::test_common_level_restricted_analytic PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_forecasts PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_forecasts_error PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_forecasts_error_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_filtered_state PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_filtered_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_predicted_state PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_predicted_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_kalman_gain PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_loglike PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothed_state PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothed_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothed_state_autocov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothed_measurement_disturbance PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothed_measurement_disturbance_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothed_state_disturbance PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothed_state_disturbance_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothing_error SKIPPED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_scaled_smoothed_estimator PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_scaled_smoothed_estimator_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_forecasts_error_diffuse_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_predicted_diffuse_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_scaled_smoothed_diffuse_estimator PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_simulation_smoothed_state XFAIL [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_simulation_smoothed_measurement_disturbance XFAIL [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_simulation_smoothed_state_disturbance XFAIL [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_nobs_diffuse PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_initialization PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_initialization_approx PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_forecasts PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_forecasts_error PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_forecasts_error_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_filtered_state PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_filtered_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_predicted_state PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_predicted_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_kalman_gain PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_loglike PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothed_state PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothed_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothed_state_autocov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothed_measurement_disturbance PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothed_measurement_disturbance_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothed_state_disturbance PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothed_state_disturbance_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothing_error SKIPPED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_scaled_smoothed_estimator PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_scaled_smoothed_estimator_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_forecasts_error_diffuse_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_predicted_diffuse_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_scaled_smoothed_diffuse_estimator PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_simulation_smoothed_state XFAIL [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_simulation_smoothed_measurement_disturbance XFAIL [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_simulation_smoothed_state_disturbance XFAIL [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_nobs_diffuse PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_initialization PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_forecasts PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_forecasts_error PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_forecasts_error_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_filtered_state PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_filtered_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_predicted_state PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_predicted_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_kalman_gain PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_loglike PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothed_state PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothed_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothed_state_autocov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothed_measurement_disturbance PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothed_state_disturbance PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothed_state_disturbance_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothing_error SKIPPED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_scaled_smoothed_estimator PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_scaled_smoothed_estimator_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_forecasts_error_diffuse_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_predicted_diffuse_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_scaled_smoothed_diffuse_estimator PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_simulation_smoothed_state XFAIL [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_simulation_smoothed_measurement_disturbance XFAIL [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_simulation_smoothed_state_disturbance XFAIL [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_nobs_diffuse PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_initialization PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_initialization_approx PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothed_measurement_disturbance_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_forecasts PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_forecasts_error PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_forecasts_error_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_filtered_state PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_filtered_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_predicted_state PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_predicted_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_kalman_gain PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_loglike PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothed_state PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothed_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothed_state_autocov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothed_measurement_disturbance PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothed_measurement_disturbance_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothed_state_disturbance PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothed_state_disturbance_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothing_error SKIPPED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_scaled_smoothed_estimator PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_scaled_smoothed_estimator_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_forecasts_error_diffuse_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_predicted_diffuse_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_scaled_smoothed_diffuse_estimator PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_simulation_smoothed_state XFAIL [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_simulation_smoothed_measurement_disturbance XFAIL [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_simulation_smoothed_state_disturbance XFAIL [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_nobs_diffuse PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_initialization PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_forecasts PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_forecasts_error PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_forecasts_error_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_filtered_state PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_filtered_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_predicted_state PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_predicted_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_kalman_gain PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_loglike PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothed_state PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothed_state_autocov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothed_measurement_disturbance PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothed_measurement_disturbance_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothed_state_disturbance PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothed_state_disturbance_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothing_error SKIPPED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_scaled_smoothed_estimator PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_scaled_smoothed_estimator_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_forecasts_error_diffuse_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_predicted_diffuse_state_cov PASSED [ 59%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_scaled_smoothed_diffuse_estimator PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_simulation_smoothed_state XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_simulation_smoothed_measurement_disturbance XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_simulation_smoothed_state_disturbance XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_initialization PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_nobs_diffuse PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_initialization_approx PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothed_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_forecasts PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_forecasts_error PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_filtered_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_filtered_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_predicted_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_predicted_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_kalman_gain PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_loglike PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothed_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothed_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothed_state_autocov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothed_measurement_disturbance PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothed_measurement_disturbance_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothed_state_disturbance PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothed_state_disturbance_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothing_error SKIPPED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_scaled_smoothed_estimator PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_scaled_smoothed_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_forecasts_error_diffuse_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_predicted_diffuse_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_scaled_smoothed_diffuse_estimator PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_simulation_smoothed_state XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_simulation_smoothed_measurement_disturbance XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_simulation_smoothed_state_disturbance XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_initialization PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_nobs_diffuse PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_forecasts_error_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_forecasts PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_forecasts_error PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_forecasts_error_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_filtered_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_filtered_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_predicted_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_predicted_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_kalman_gain PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_loglike PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothed_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothed_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothed_state_autocov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothed_measurement_disturbance PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothed_measurement_disturbance_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothed_state_disturbance PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothed_state_disturbance_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothing_error SKIPPED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_scaled_smoothed_estimator PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_scaled_smoothed_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_forecasts_error_diffuse_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_predicted_diffuse_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_scaled_smoothed_diffuse_estimator PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_simulation_smoothed_state XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_simulation_smoothed_measurement_disturbance XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_simulation_smoothed_state_disturbance XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_nobs_diffuse PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_initialization PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_initialization_approx PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_forecasts PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_forecasts_error PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_forecasts_error_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_filtered_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_predicted_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_kalman_gain PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_loglike PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothed_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothed_state_autocov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothed_measurement_disturbance PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothed_measurement_disturbance_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothed_state_disturbance PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothed_state_disturbance_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothing_error SKIPPED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_scaled_smoothed_estimator PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_scaled_smoothed_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_forecasts_error_diffuse_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_predicted_diffuse_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_scaled_smoothed_diffuse_estimator PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_simulation_smoothed_state XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_simulation_smoothed_measurement_disturbance XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_simulation_smoothed_state_disturbance XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_nobs_diffuse PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_initialization PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_predicted_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_filtered_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothed_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_forecasts PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_forecasts_error PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_forecasts_error_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_filtered_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_filtered_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_predicted_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_predicted_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_kalman_gain PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_loglike PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothed_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothed_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothed_state_autocov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothed_measurement_disturbance PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothed_measurement_disturbance_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothed_state_disturbance PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothed_state_disturbance_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothing_error SKIPPED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_scaled_smoothed_estimator PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_scaled_smoothed_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_forecasts_error_diffuse_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_predicted_diffuse_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_scaled_smoothed_diffuse_estimator PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_simulation_smoothed_state XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_simulation_smoothed_measurement_disturbance XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_simulation_smoothed_state_disturbance XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_nobs_diffuse PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_initialization PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_initialization_approx PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_forecasts PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_forecasts_error PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_forecasts_error_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_filtered_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_predicted_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_kalman_gain PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_loglike PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothed_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothed_state_autocov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothed_measurement_disturbance PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothed_measurement_disturbance_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothed_state_disturbance PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothed_state_disturbance_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothing_error SKIPPED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_scaled_smoothed_estimator PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_scaled_smoothed_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_forecasts_error_diffuse_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_predicted_diffuse_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_scaled_smoothed_diffuse_estimator PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_simulation_smoothed_state XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_simulation_smoothed_measurement_disturbance XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_simulation_smoothed_state_disturbance XFAIL [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_nobs_diffuse PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_initialization PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_predicted_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_filtered_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothed_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_forecasts PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_forecasts_error PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_forecasts_error_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_filtered_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_filtered_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_predicted_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_predicted_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_kalman_gain PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_loglike PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothed_state PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothed_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothed_state_autocov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothed_measurement_disturbance PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothed_measurement_disturbance_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothed_state_disturbance PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothed_state_disturbance_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothing_error SKIPPED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_scaled_smoothed_estimator PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_scaled_smoothed_estimator_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_forecasts_error_diffuse_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_predicted_diffuse_state_cov PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_scaled_smoothed_diffuse_estimator PASSED [ 60%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 60%] 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statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::test_irrelevant_state XFAIL [ 61%] statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::test_nondiagonal_obs_cov PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed02::test_fitted PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed02::test_output PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed02::test_forecasts PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed02::test_conf_int PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed02::test_initial_states PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed02::test_states PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed02::test_misc PASSED [ 61%] 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statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESHeuristicInitialization::test_heuristic PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltHeuristicInitialization::test_heuristic PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedHeuristicInitialization::test_heuristic PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersHeuristicInitialization::test_heuristic PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersDampedHeuristicInitialization::test_heuristic PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersNoTrendHeuristicInitialization::test_heuristic PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::test_concentrated_initialization PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESConcentratedInitialization::test_given_params PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESConcentratedInitialization::test_estimated_params PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltConcentratedInitialization::test_given_params PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltConcentratedInitialization::test_estimated_params PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedConcentratedInitialization::test_given_params PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedConcentratedInitialization::test_estimated_params PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersConcentratedInitialization::test_given_params PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersConcentratedInitialization::test_estimated_params PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersDampedConcentratedInitialization::test_given_params PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersDampedConcentratedInitialization::test_estimated_params PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersNoTrendConcentratedInitialization::test_given_params PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersNoTrendConcentratedInitialization::test_estimated_params PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestMultiIndex::test_fitted PASSED [ 61%] statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestMultiIndex::test_output PASSED [ 61%] 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statsmodels/tsa/statespace/tests/test_fixed_params.py::test_results_extend PASSED [ 61%] statsmodels/tsa/statespace/tests/test_fixed_params.py::test_results_apply PASSED [ 61%] statsmodels/tsa/statespace/tests/test_fixed_params.py::test_mle_validate PASSED [ 61%] statsmodels/tsa/statespace/tests/test_fixed_params.py::test_sarimax_validate PASSED [ 61%] statsmodels/tsa/statespace/tests/test_fixed_params.py::test_structural_validate PASSED [ 61%] statsmodels/tsa/statespace/tests/test_fixed_params.py::test_dynamic_factor_validate PASSED [ 61%] statsmodels/tsa/statespace/tests/test_fixed_params.py::test_varmax_validate PASSED [ 61%] statsmodels/tsa/statespace/tests/test_fixed_params.py::test_sarimax_nonconsecutive PASSED [ 61%] statsmodels/tsa/statespace/tests/test_fixed_params.py::test_structural PASSED [ 61%] statsmodels/tsa/statespace/tests/test_fixed_params.py::test_dynamic_factor_diag_error_cov PASSED [ 61%] statsmodels/tsa/statespace/tests/test_fixed_params.py::test_score_shape PASSED [ 61%] statsmodels/tsa/statespace/tests/test_forecasting.py::test_append_multistep[list] PASSED [ 61%] statsmodels/tsa/statespace/tests/test_forecasting.py::test_append_multistep[numpy] PASSED [ 61%] statsmodels/tsa/statespace/tests/test_forecasting.py::test_append_multistep[range] PASSED [ 61%] statsmodels/tsa/statespace/tests/test_forecasting.py::test_append_multistep[date] PASSED [ 61%] statsmodels/tsa/statespace/tests/test_forecasting.py::test_append_multistep[period] PASSED [ 61%] statsmodels/tsa/statespace/tests/test_impulse_responses.py::test_sarimax PASSED [ 61%] statsmodels/tsa/statespace/tests/test_impulse_responses.py::test_structural PASSED [ 61%] statsmodels/tsa/statespace/tests/test_impulse_responses.py::test_varmax PASSED [ 61%] statsmodels/tsa/statespace/tests/test_impulse_responses.py::test_dynamic_factor PASSED [ 61%] statsmodels/tsa/statespace/tests/test_impulse_responses.py::test_time_varying_ssm PASSED [ 61%] statsmodels/tsa/statespace/tests/test_impulse_responses.py::test_time_varying_in_sample PASSED [ 61%] statsmodels/tsa/statespace/tests/test_impulse_responses.py::test_time_varying_out_of_sample PASSED [ 61%] statsmodels/tsa/statespace/tests/test_impulse_responses.py::test_time_varying_in_sample_anchored PASSED [ 61%] statsmodels/tsa/statespace/tests/test_impulse_responses.py::test_time_varying_out_of_sample_anchored PASSED [ 61%] statsmodels/tsa/statespace/tests/test_impulse_responses.py::test_time_varying_out_of_sample_anchored_end PASSED [ 61%] statsmodels/tsa/statespace/tests/test_impulse_responses.py::test_pandas_univariate_rangeindex PASSED [ 61%] statsmodels/tsa/statespace/tests/test_impulse_responses.py::test_pandas_univariate_dateindex PASSED [ 61%] statsmodels/tsa/statespace/tests/test_impulse_responses.py::test_pandas_multivariate_rangeindex PASSED [ 61%] statsmodels/tsa/statespace/tests/test_impulse_responses.py::test_pandas_multivariate_dateindex PASSED [ 61%] 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statsmodels/tsa/statespace/tests/test_kalman.py::TestClark1989ConserveAll::test_loglike PASSED [ 62%] statsmodels/tsa/statespace/tests/test_kalman.py::TestClark1989ConserveAll::test_filtered_state PASSED [ 62%] statsmodels/tsa/statespace/tests/test_kalman.py::test_stationary_initialization PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_init_matrices_time_invariant PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_init_matrices_time_varying PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_wrapping PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_fit_misc PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_score_misc PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_from_formula PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_score_analytic_ar1 PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_cov_params PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_transform PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_filter PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_params PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_results PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_predict PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_forecast PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_summary PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_basic_endog PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_numpy_endog PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_pandas_endog PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_diagnostics PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_small_sample_serial_correlation_test PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_diagnostics_nile_eviews PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_diagnostics_nile_durbinkoopman PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_prediction_results PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_lutkepohl_information_criteria PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_append_extend_apply_invalid PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_integer_params PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_states_index_periodindex PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_states_index_dateindex PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_states_index_int64index PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_states_index_rangeindex PASSED [ 62%] statsmodels/tsa/statespace/tests/test_mlemodel.py::test_invalid_kwargs PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestIntercepts::test_loglike PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestIntercepts::test_scaled_smoothed_estimator PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestIntercepts::test_scaled_smoothed_estimator_cov PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestIntercepts::test_forecasts PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestIntercepts::test_forecasts_error PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestIntercepts::test_forecasts_error_cov PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestIntercepts::test_predicted_states PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestIntercepts::test_predicted_states_cov PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestIntercepts::test_smoothed_states PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestIntercepts::test_smoothed_states_cov PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestIntercepts::test_smoothed_forecasts PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestIntercepts::test_smoothed_state_disturbance PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestIntercepts::test_smoothed_state_disturbance_cov PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestIntercepts::test_smoothed_measurement_disturbance PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestIntercepts::test_smoothed_measurement_disturbance_cov PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::test_large_kposdef PASSED [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestLargeStateCovAR1::test_dimensions SKIPPED_posdef > k_states. However, this test could be used if models of those types were allowed) [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestLargeStateCovAR1::test_loglike SKIPPED_posdef > k_states. However, this test could be used if models of those types were allowed) [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestLargeStateCovAR1::test_scaled_smoothed_estimator SKIPPED_posdef > k_states. However, this test could be used if models of those types were allowed) [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestLargeStateCovAR1::test_scaled_smoothed_estimator_cov SKIPPED_posdef > k_states. However, this test could be used if models of those types were allowed) [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestLargeStateCovAR1::test_forecasts SKIPPED_posdef > k_states. However, this test could be used if models of those types were allowed) [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestLargeStateCovAR1::test_forecasts_error SKIPPED_posdef > k_states. However, this test could be used if models of those types were allowed) [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestLargeStateCovAR1::test_forecasts_error_cov SKIPPED_posdef > k_states. However, this test could be used if models of those types were allowed) [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestLargeStateCovAR1::test_predicted_states SKIPPED_posdef > k_states. However, this test could be used if models of those types were allowed) [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestLargeStateCovAR1::test_predicted_states_cov SKIPPED_posdef > k_states. However, this test could be used if models of those types were allowed) [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestLargeStateCovAR1::test_smoothed_states SKIPPED_posdef > k_states. However, this test could be used if models of those types were allowed) [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestLargeStateCovAR1::test_smoothed_states_cov SKIPPED_posdef > k_states. However, this test could be used if models of those types were allowed) [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestLargeStateCovAR1::test_smoothed_state_disturbance SKIPPED_posdef > k_states. However, this test could be used if models of those types were allowed) [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestLargeStateCovAR1::test_smoothed_state_disturbance_cov SKIPPED_posdef > k_states. However, this test could be used if models of those types were allowed) [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestLargeStateCovAR1::test_smoothed_measurement_disturbance SKIPPED_posdef > k_states. However, this test could be used if models of those types were allowed) [ 62%] statsmodels/tsa/statespace/tests/test_models.py::TestLargeStateCovAR1::test_smoothed_measurement_disturbance_cov SKIPPED_posdef > k_states. However, this test could be used if models of those types were allowed) [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_basic[None] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_basic[init] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_basic[mixed] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_basic[all] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[0-stationary-None-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[0-stationary-None-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[0-stationary-init-True] PASSED [ 62%] 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statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[0-approximate_diffuse-None-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[0-approximate_diffuse-init-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[0-approximate_diffuse-init-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[0-approximate_diffuse-mixed-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[0-approximate_diffuse-mixed-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[0-approximate_diffuse-all-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[0-approximate_diffuse-all-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods1-stationary-None-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods1-stationary-None-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods1-stationary-init-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods1-stationary-init-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods1-stationary-mixed-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods1-stationary-mixed-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods1-stationary-all-True] PASSED [ 62%] 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statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods1-approximate_diffuse-mixed-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods1-approximate_diffuse-all-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods1-approximate_diffuse-all-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-stationary-None-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-stationary-None-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-stationary-init-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-stationary-init-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-stationary-mixed-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-stationary-mixed-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-stationary-all-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-stationary-all-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-diffuse-None-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-diffuse-None-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-diffuse-init-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-diffuse-init-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-diffuse-mixed-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-diffuse-mixed-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-diffuse-all-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-diffuse-all-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-approximate_diffuse-None-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-approximate_diffuse-None-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-approximate_diffuse-init-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-approximate_diffuse-init-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-approximate_diffuse-mixed-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-approximate_diffuse-mixed-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-approximate_diffuse-all-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_filter_output[periods2-approximate_diffuse-all-False] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-stationary-None-True] PASSED [ 62%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-stationary-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-stationary-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-stationary-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-stationary-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-stationary-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-stationary-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-stationary-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-diffuse-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-diffuse-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-diffuse-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-diffuse-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-diffuse-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-diffuse-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-diffuse-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-diffuse-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-approximate_diffuse-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-approximate_diffuse-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-approximate_diffuse-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-approximate_diffuse-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-approximate_diffuse-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-approximate_diffuse-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-approximate_diffuse-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-0-approximate_diffuse-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-stationary-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-stationary-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-stationary-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-stationary-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-stationary-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-stationary-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-stationary-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-stationary-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-diffuse-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-diffuse-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-diffuse-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-diffuse-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-diffuse-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-diffuse-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-diffuse-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-diffuse-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-approximate_diffuse-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-approximate_diffuse-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-approximate_diffuse-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-approximate_diffuse-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-approximate_diffuse-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-approximate_diffuse-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-approximate_diffuse-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods1-approximate_diffuse-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-stationary-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-stationary-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-stationary-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-stationary-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-stationary-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-stationary-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-stationary-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-stationary-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-diffuse-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-diffuse-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-diffuse-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-diffuse-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-diffuse-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-diffuse-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-diffuse-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-diffuse-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-approximate_diffuse-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-approximate_diffuse-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-approximate_diffuse-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-approximate_diffuse-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-approximate_diffuse-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-approximate_diffuse-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-approximate_diffuse-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[None-periods2-approximate_diffuse-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-stationary-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-stationary-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-stationary-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-stationary-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-stationary-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-stationary-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-stationary-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-stationary-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-diffuse-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-diffuse-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-diffuse-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-diffuse-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-diffuse-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-diffuse-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-diffuse-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-diffuse-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-approximate_diffuse-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-approximate_diffuse-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-approximate_diffuse-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-approximate_diffuse-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-approximate_diffuse-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-approximate_diffuse-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-approximate_diffuse-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-0-approximate_diffuse-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-stationary-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-stationary-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-stationary-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-stationary-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-stationary-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-stationary-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-stationary-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-stationary-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-diffuse-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-diffuse-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-diffuse-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-diffuse-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-diffuse-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-diffuse-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-diffuse-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-diffuse-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-approximate_diffuse-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-approximate_diffuse-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-approximate_diffuse-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-approximate_diffuse-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-approximate_diffuse-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-approximate_diffuse-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-approximate_diffuse-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods1-approximate_diffuse-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-stationary-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-stationary-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-stationary-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-stationary-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-stationary-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-stationary-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-stationary-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-stationary-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-diffuse-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-diffuse-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-diffuse-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-diffuse-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-diffuse-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-diffuse-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-diffuse-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-diffuse-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-approximate_diffuse-None-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-approximate_diffuse-None-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-approximate_diffuse-init-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-approximate_diffuse-init-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-approximate_diffuse-mixed-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-approximate_diffuse-mixed-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-approximate_diffuse-all-True] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_smoother_output[alternate_timing-periods2-approximate_diffuse-all-False] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_invalid_options PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[False-0-None] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[False-0-init] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[False-0-mixed] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[False-0-all] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[False-periods1-None] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[False-periods1-init] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[False-periods1-mixed] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[False-periods1-all] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[False-periods2-None] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[False-periods2-init] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[False-periods2-mixed] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[False-periods2-all] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[True-0-None] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[True-0-init] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[True-0-mixed] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[True-0-all] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[True-periods1-None] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[True-periods1-init] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[True-periods1-mixed] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[True-periods1-all] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[True-periods2-None] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[True-periods2-init] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[True-periods2-mixed] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_sarimax[True-periods2-all] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[False-0-None] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[False-0-init] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[False-0-mixed] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[False-0-all] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[False-periods1-None] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[False-periods1-init] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[False-periods1-mixed] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[False-periods1-all] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[False-periods2-None] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[False-periods2-init] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[False-periods2-mixed] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[False-periods2-all] PASSED [ 63%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[True-0-None] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[True-0-init] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[True-0-mixed] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[True-0-all] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[True-periods1-None] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[True-periods1-init] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[True-periods1-mixed] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[True-periods1-all] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[True-periods2-None] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[True-periods2-init] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[True-periods2-mixed] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_unobserved_components[True-periods2-all] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_varmax[0-None] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_varmax[0-init] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_varmax[0-mixed] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_varmax[0-all] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_varmax[periods1-None] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_varmax[periods1-init] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_varmax[periods1-mixed] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_varmax[periods1-all] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_varmax[periods2-None] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_varmax[periods2-init] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_varmax[periods2-mixed] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_varmax[periods2-all] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_dynamic_factor[0-None] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_dynamic_factor[0-init] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_dynamic_factor[0-mixed] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_dynamic_factor[0-all] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_dynamic_factor[periods1-None] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_dynamic_factor[periods1-init] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_dynamic_factor[periods1-mixed] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_dynamic_factor[periods1-all] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_dynamic_factor[periods2-None] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_dynamic_factor[periods2-init] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_dynamic_factor[periods2-mixed] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_dynamic_factor[periods2-all] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_simulation_smoothing[None] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_simulation_smoothing[mixed] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py::test_time_varying_model PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_invariant[True-True-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_invariant[True-True-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_invariant[True-False-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_invariant[True-False-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_invariant[False-True-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_invariant[False-True-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_invariant[False-False-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_invariant[False-False-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_invariant[-2-True-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_invariant[-2-True-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_invariant[-2-False-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_invariant[-2-False-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_varying[exog-True-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_varying[exog-True-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_varying[exog-False-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_varying[exog-False-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_varying[trend-True-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_varying[trend-True-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_varying[trend-False-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_sarimax_time_varying[trend-False-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_unobserved_components_time_varying[True-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_unobserved_components_time_varying[True-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_unobserved_components_time_varying[False-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_unobserved_components_time_varying[False-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_varmax_time_invariant[True-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_varmax_time_invariant[True-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_varmax_time_invariant[False-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_varmax_time_invariant[False-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_varmax_time_varying[exog-True-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_varmax_time_varying[exog-True-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_varmax_time_varying[exog-False-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_varmax_time_varying[exog-False-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_varmax_time_varying[trend-True-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_varmax_time_varying[trend-True-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_varmax_time_varying[trend-False-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_varmax_time_varying[trend-False-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_dynamic_factor_time_varying[True-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_dynamic_factor_time_varying[True-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_dynamic_factor_time_varying[False-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_dynamic_factor_time_varying[False-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_defaults[True-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_defaults[True-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_defaults[False-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_defaults[False-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_comparison_types PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_start_end_dates[True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_start_end_dates[False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_start_end_int[range] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_start_end_int[range2] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_start_end_int[int64] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_start_end_int[numpy] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_start_end_int[list] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_invalid PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_detailed_revisions[True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_detailed_revisions[-10] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_detailed_revisions[200] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_grouped_revisions[False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_grouped_revisions[202] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_mixed_revisions[-1] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_news.py::test_mixed_revisions[201] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_options.py::TestOptions::test_filter_methods PASSED [ 64%] statsmodels/tsa/statespace/tests/test_options.py::TestOptions::test_inversion_methods PASSED [ 64%] statsmodels/tsa/statespace/tests/test_options.py::TestOptions::test_stability_methods PASSED [ 64%] statsmodels/tsa/statespace/tests/test_options.py::TestOptions::test_conserve_memory PASSED [ 64%] statsmodels/tsa/statespace/tests/test_options.py::TestOptions::test_smoother_outputs PASSED [ 64%] statsmodels/tsa/statespace/tests/test_options.py::TestOptions::test_simulation_outputs PASSED [ 64%] statsmodels/tsa/statespace/tests/test_pickle.py::test_pickle_fit_sarimax PASSED [ 64%] statsmodels/tsa/statespace/tests/test_pickle.py::test_unobserved_components_pickle PASSED [ 64%] statsmodels/tsa/statespace/tests/test_pickle.py::test_kalman_filter_pickle PASSED [ 64%] statsmodels/tsa/statespace/tests/test_pickle.py::test_representation_pickle PASSED [ 64%] statsmodels/tsa/statespace/tests/test_prediction.py::test_predict_dates PASSED [ 64%] statsmodels/tsa/statespace/tests/test_prediction.py::test_memory_no_predicted PASSED [ 64%] statsmodels/tsa/statespace/tests/test_prediction.py::test_concatenated_predict_sarimax[n-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_prediction.py::test_concatenated_predict_sarimax[n-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_prediction.py::test_concatenated_predict_sarimax[c-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_prediction.py::test_concatenated_predict_sarimax[c-False] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_prediction.py::test_concatenated_predict_sarimax[t-True] PASSED [ 64%] statsmodels/tsa/statespace/tests/test_prediction.py::test_concatenated_predict_sarimax[t-False] PASSED [ 64%] 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65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_trend_ct::test_start_params PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_trend_ct::test_transform_untransform PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_trend_ct::test_results PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_trend_ct::test_plot_diagnostics PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_trend_ct::test_predict PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_trend_ct::test_init_keys_replicate PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_trend_polynomial::test_loglike PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_trend_polynomial::test_start_params PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_trend_polynomial::test_transform_untransform PASSED [ 65%] 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[ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_diff::test_init_keys_replicate PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_seasonal_diff::test_loglike PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_seasonal_diff::test_start_params PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_seasonal_diff::test_transform_untransform PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_seasonal_diff::test_results PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_seasonal_diff::test_plot_diagnostics PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_seasonal_diff::test_predict PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_seasonal_diff::test_init_keys_replicate PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_diffuse::test_loglike PASSED [ 65%] 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PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_no_enforce::test_plot_diagnostics PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_no_enforce::test_predict PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_no_enforce::test_init_keys_replicate PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_exogenous::test_loglike PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_exogenous::test_start_params PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_exogenous::test_transform_untransform PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_exogenous::test_results PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_exogenous::test_plot_diagnostics PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_exogenous::test_predict PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_exogenous::test_init_keys_replicate PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_exogenous_in_state::test_start_params PASSED [ 65%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_exogenous_in_state::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_exogenous_in_state::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_exogenous_in_state::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_exogenous_in_state::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_exogenous_in_state::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_exogenous_in_state::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ar_exogenous_in_state::test_regression_coefficient PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_as_polynomial::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_as_polynomial::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_as_polynomial::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_as_polynomial::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_as_polynomial::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_as_polynomial::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_as_polynomial::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_c::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_c::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_c::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_c::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_c::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_c::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_c::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_ct::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_ct::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_ct::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_ct::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_ct::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_ct::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_ct::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_polynomial::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_polynomial::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_polynomial::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_polynomial::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_polynomial::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_polynomial::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_trend_polynomial::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_diff::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_diff::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_diff::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_diff::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_diff::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_diff::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_diff::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_seasonal_diff::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_seasonal_diff::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_seasonal_diff::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_seasonal_diff::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_seasonal_diff::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_seasonal_diff::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_seasonal_diff::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_diffuse::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_diffuse::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_diffuse::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_diffuse::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_diffuse::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_diffuse::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_diffuse::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_exogenous::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_exogenous::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_exogenous::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_exogenous::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_exogenous::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_exogenous::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_ma_exogenous::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_c::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_c::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_c::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_c::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_c::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_c::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_c::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_ct::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_ct::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_ct::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_ct::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_ct::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_ct::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_ct::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_polynomial::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_polynomial::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_polynomial::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_polynomial::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_polynomial::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_polynomial::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_trend_polynomial::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diff::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diff::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diff::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diff::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diff::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diff::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diff::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_seasonal_diff::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_seasonal_diff::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_seasonal_diff::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_seasonal_diff::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_seasonal_diff::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_seasonal_diff::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_seasonal_diff::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diff_seasonal_diff::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diff_seasonal_diff::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diff_seasonal_diff::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diff_seasonal_diff::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diff_seasonal_diff::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diff_seasonal_diff::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diff_seasonal_diff::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diffuse::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diffuse::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diffuse::test_transform_untransform PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diffuse::test_results PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diffuse::test_plot_diagnostics PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diffuse::test_predict PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_diffuse::test_init_keys_replicate PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_exogenous::test_loglike PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_exogenous::test_start_params PASSED [ 66%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_arma_exogenous::test_transform_untransform PASSED [ 66%] 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PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ar_diffuse::test_loglike PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ar_diffuse::test_start_params PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ar_diffuse::test_transform_untransform PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ar_diffuse::test_results PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ar_diffuse::test_plot_diagnostics PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ar_diffuse::test_predict PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ar_diffuse::test_init_keys_replicate PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ar_exogenous::test_loglike PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ar_exogenous::test_start_params PASSED [ 67%] 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PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ma_diff::test_plot_diagnostics PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ma_diff::test_predict PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ma_diff::test_init_keys_replicate PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ma_seasonal_diff::test_loglike PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ma_seasonal_diff::test_start_params PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ma_seasonal_diff::test_transform_untransform PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ma_seasonal_diff::test_results PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_ma_seasonal_diff::test_plot_diagnostics PASSED [ 67%] 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statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_arma_diff::test_loglike PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_arma_diff::test_start_params PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_arma_diff::test_transform_untransform PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_arma_diff::test_results PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_arma_diff::test_plot_diagnostics PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_arma_diff::test_predict PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_arma_diff::test_init_keys_replicate PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_arma_seasonal_diff::test_loglike PASSED [ 67%] statsmodels/tsa/statespace/tests/test_sarimax.py::Test_seasonal_arma_seasonal_diff::test_start_params PASSED [ 67%] 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statsmodels/tsa/statespace/tests/test_weights.py::test_compute_t_compute_j[compute_t0-compute_j2] PASSED [ 72%] statsmodels/tsa/statespace/tests/test_weights.py::test_compute_t_compute_j[compute_t0-8] PASSED [ 72%] statsmodels/tsa/statespace/tests/test_weights.py::test_compute_t_compute_j[compute_t1-compute_j0] PASSED [ 72%] statsmodels/tsa/statespace/tests/test_weights.py::test_compute_t_compute_j[compute_t1-compute_j1] PASSED [ 72%] statsmodels/tsa/statespace/tests/test_weights.py::test_compute_t_compute_j[compute_t1-compute_j2] PASSED [ 72%] statsmodels/tsa/statespace/tests/test_weights.py::test_compute_t_compute_j[compute_t1-8] PASSED [ 72%] statsmodels/tsa/statespace/tests/test_weights.py::test_compute_t_compute_j[compute_t2-compute_j0] PASSED [ 72%] statsmodels/tsa/statespace/tests/test_weights.py::test_compute_t_compute_j[compute_t2-compute_j1] PASSED [ 72%] statsmodels/tsa/statespace/tests/test_weights.py::test_compute_t_compute_j[compute_t2-compute_j2] PASSED [ 72%] statsmodels/tsa/statespace/tests/test_weights.py::test_compute_t_compute_j[compute_t2-8] PASSED [ 72%] statsmodels/tsa/statespace/tests/test_weights.py::test_compute_t_compute_j[5-compute_j0] PASSED [ 72%] statsmodels/tsa/statespace/tests/test_weights.py::test_compute_t_compute_j[5-compute_j1] PASSED [ 72%] statsmodels/tsa/statespace/tests/test_weights.py::test_compute_t_compute_j[5-compute_j2] PASSED [ 72%] statsmodels/tsa/statespace/tests/test_weights.py::test_compute_t_compute_j[5-8] PASSED [ 72%] statsmodels/tsa/statespace/tests/test_weights.py::test_resmooth PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_return_pandas_series_when_input_pandas_and_len_periods_one PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_seasonal_is_datafame_when_input_pandas_and_multiple_periods PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_number_of_seasonal_components[data-3-None-1] PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_number_of_seasonal_components[data-periods1-None-2] PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_number_of_seasonal_components[data-periods2-None-2] PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_raise_value_error_when_periods_and_windows_diff_lengths[periods0-1] PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_raise_value_error_when_periods_and_windows_diff_lengths[7-windows1] PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_fit_with_box_cox[data-0.1] PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_fit_with_box_cox[data-1] PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_fit_with_box_cox[data--3.0] PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_fit_with_box_cox[data-auto] PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_auto_fit_with_box_cox PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_stl_kwargs_smoke PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_plot PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_output_similar_to_R_implementation PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_output_invariant_to_period_order[data-periods_ordered0-windows_ordered0-periods_not_ordered0-windows_not_ordered0] PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_output_invariant_to_period_order[data-periods_ordered1-windows_ordered1-periods_not_ordered1-windows_not_ordered1] PASSED [ 72%] statsmodels/tsa/stl/tests/test_mstl.py::test_output_invariant_to_period_order[data-periods_ordered2-None-periods_not_ordered2-None] PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_baseline_class PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_short_class PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_nljump_1_class PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_ntjump_1_class PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_nljump_1_ntjump_1_class PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_parameter_checks_period PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_parameter_checks_seasonal PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_parameter_checks_trend PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_parameter_checks_low_pass PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_jump_errors PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_defaults_smoke[True] PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_pandas[True] PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_defaults_smoke[False] PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_pandas[False] PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_period_detection PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_no_period PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_plot PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_default_trend PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_pickle PASSED [ 72%] statsmodels/tsa/stl/tests/test_stl.py::test_squezable_to_1d PASSED [ 72%] statsmodels/tsa/tests/test_adfuller_lag.py::test_adf_autolag PASSED [ 72%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-bse] PASSED [ 72%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 72%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 72%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 72%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 72%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-llf] PASSED [ 72%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 72%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-params] PASSED [ 72%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-resid] PASSED [ 72%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-scale] PASSED [ 72%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-bse] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-cov_params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_model] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-llf] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-nobs] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-scale] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-tvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-use_t] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 73%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 74%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-bse] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-llf] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-scale] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-bse] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-cov_params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_model] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-llf] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-nobs] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-scale] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-tvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-use_t] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 75%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 76%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-bse] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-llf] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-scale] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-bse] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-cov_params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-df_model] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-df_resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-llf] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-nobs] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-scale] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-tvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-use_t] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 77%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-bse] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-llf] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-scale] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-bse] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-cov_params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_model] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-llf] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-nobs] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-scale] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-tvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-use_t] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 78%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-bse] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-llf] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-scale] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-bse] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-cov_params] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-df_model] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-df_resid] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 79%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-llf] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-nobs] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-scale] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-tvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-use_t] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 80%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-bse] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-llf] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-scale] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-bse] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-cov_params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_model] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-llf] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-nobs] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-scale] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-tvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-use_t] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 81%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-bse] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-llf] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-scale] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-bse] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-cov_params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-df_model] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-df_resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-llf] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-nobs] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-scale] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-tvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-use_t] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 82%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 83%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back: None] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back: 12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back: None] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back: 12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back: None] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back: 12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back: None] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back: 12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back: None] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back: 12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back: None] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back: 12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back: None] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back: 12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 89%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 91%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_parameterless_autoreg PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_predict_errors PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_spec_errors PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_ar_select_order_smoke PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSConstant::test_params PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSConstant::test_llf PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSConstant::test_fpe PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSConstant::test_pickle PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSConstant::test_summary PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSConstant::test_pvalues PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSConstant::test_bse PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSConstant::test_predict PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSNoConstant::test_params PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSNoConstant::test_llf PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSNoConstant::test_fpe PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSNoConstant::test_pickle PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSNoConstant::test_summary PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSNoConstant::test_pvalues PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSNoConstant::test_bse PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSNoConstant::test_predict PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag0] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag1] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag2] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag3] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag4] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag5] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag6] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag7] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag8] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag9] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag10] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag11] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag12] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag13] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag14] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_info_criterion[lag15] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_named_series[True] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_named_series[False] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_series PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_ar_order_select PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_constant_column_trend PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_summary_corner[True] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_summary_corner[False] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_score PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_roots PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_equiv_dynamic PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_dynamic_against_sarimax PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_predict_seasonal PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_predict_exog PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_predict_irregular_ar PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_forecast_start_end_equiv[True] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_forecast_start_end_equiv[False] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_start[21] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_start[25] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_deterministic PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_forecast_equiv PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_forecast_period_index PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_plot_err PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_resids PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_dynamic_predictions PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_dynamic_predictions_oos PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_invalid_dynamic PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_exog_prediction PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_old_names PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_diagnostic_summary_short PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_ar_model_predict PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_no_variables PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_removal PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_apply_exception PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[True-0-True-n] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[True-0-True-ct] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[True-0-False-n] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[True-0-False-ct] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[True-1-True-n] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[True-1-True-ct] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[True-1-False-n] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[True-1-False-ct] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[True-3-True-n] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[True-3-True-ct] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[True-3-False-n] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[True-3-False-ct] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[False-0-True-n] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[False-0-True-ct] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[False-0-False-n] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[False-0-False-ct] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[False-1-True-n] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[False-1-True-ct] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[False-1-False-n] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[False-1-False-ct] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[False-3-True-n] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[False-3-True-ct] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[False-3-False-n] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append[False-3-False-ct] PASSED [ 94%] statsmodels/tsa/tests/test_ar.py::test_autoreg_append_deterministic PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_acovf PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_acovf_persistent PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_acf PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_acf_compare_R_ARMAacf PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_acov_compare_theoretical_arma_acov PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma0-ar0] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma0-ar1] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma0-ar2] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma0-ar3] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma1-ar0] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma1-ar1] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma1-ar2] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma1-ar3] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma2-ar0] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma2-ar1] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma2-ar2] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma2-ar3] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma3-ar0] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma3-ar1] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma3-ar2] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_generate_sample[standard_normal-ma3-ar3] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_fi PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_arma_impulse_response PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma0-ar0] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma0-ar1] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma0-ar2] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma0-ar3] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma1-ar0] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma1-ar1] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma1-ar2] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma1-ar3] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma2-ar0] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma2-ar1] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma2-ar2] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma2-ar3] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma3-ar0] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma3-ar1] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma3-ar2] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_spectrum[ma3-ar3] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma0-ar0] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma0-ar1] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma0-ar2] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma0-ar3] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma1-ar0] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma1-ar1] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma1-ar2] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma1-ar3] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma2-ar0] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma2-ar1] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma2-ar2] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma2-ar3] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma3-ar0] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma3-ar1] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma3-ar2] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_armafft[ma3-ar3] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_lpol2index_index2lpol PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::TestArmaProcess::test_empty_coeff PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::TestArmaProcess::test_from_roots PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::TestArmaProcess::test_from_coeff PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::TestArmaProcess::test_process_multiplication PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::TestArmaProcess::test_str_repr PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::TestArmaProcess::test_acf PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::TestArmaProcess::test_pacf PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::TestArmaProcess::test_isstationary PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::TestArmaProcess::test_arma2ar PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::TestArmaProcess::test_invertroots PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::TestArmaProcess::test_generate_sample PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::TestArmaProcess::test_impulse_response PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::TestArmaProcess::test_periodogram PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_from_estimation[True-0] PASSED [ 94%] statsmodels/tsa/tests/test_arima_process.py::test_from_estimation[True-1] PASSED [ 94%] statsmodels/tsa/tests/test_bds.py::TestBDSSequence::test_stat PASSED [ 94%] statsmodels/tsa/tests/test_bds.py::TestBDSSequence::test_pvalue PASSED [ 94%] statsmodels/tsa/tests/test_bds.py::TestBDSNormal::test_stat PASSED [ 94%] statsmodels/tsa/tests/test_bds.py::TestBDSNormal::test_pvalue PASSED [ 94%] statsmodels/tsa/tests/test_bds.py::TestBDSCombined::test_stat PASSED [ 94%] statsmodels/tsa/tests/test_bds.py::TestBDSCombined::test_pvalue PASSED [ 94%] statsmodels/tsa/tests/test_bds.py::TestBDSGDPC1::test_stat PASSED [ 94%] statsmodels/tsa/tests/test_bds.py::TestBDSGDPC1::test_pvalue PASSED [ 94%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend_smoke[range-None] PASSED [ 94%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_smoke[range-None] PASSED [ 94%] statsmodels/tsa/tests/test_deterministic.py::test_fourier_smoke[range-None] PASSED [ 94%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend_smoke[range-False] PASSED [ 94%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_smoke[range-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier_smoke[range-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend_smoke[period-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_smoke[period-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier_smoke[period-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend_smoke[period-None] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_smoke[period-None] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier_smoke[period-None] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend_smoke[period-list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_smoke[period-list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier_smoke[period-list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend_smoke[range-list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_smoke[range-list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier_smoke[range-list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend_smoke[datetime-list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_smoke[datetime-list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier_smoke[datetime-list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend_smoke[datetime-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_smoke[datetime-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier_smoke[datetime-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend_smoke[datetime-None] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_smoke[datetime-None] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier_smoke[datetime-None] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_invalid_formcast_index[datetime] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend[datetime] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality[datetime] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier[datetime] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend_smoke[fib-list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_smoke[fib-list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier_smoke[fib-list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend_smoke[fib-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_smoke[fib-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier_smoke[fib-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend_smoke[fib-None] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_smoke[fib-None] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier_smoke[fib-None] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_invalid_formcast_index[fib] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend[fib] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality[fib] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier[fib] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend_smoke[int64-list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_smoke[int64-list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier_smoke[int64-list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend_smoke[int64-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_smoke[int64-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier_smoke[int64-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend_smoke[int64-None] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_smoke[int64-None] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier_smoke[int64-None] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_invalid_formcast_index[int64] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend[int64] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality[int64] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier[int64] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_time_trend_smoke[list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_fourier_smoke[list] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[list-freq_period0] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[list-freq_period1] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[list-freq_period2] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[list-freq_period3] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[list-freq_period4] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[list-freq_period5] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[list-freq_period6] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[list-freq_period7] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[list-freq_period8] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[list-freq_period9] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[list-freq_period10] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[list-freq_period11] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[list-freq_period12] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[list-freq_period13] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[list-freq_period14] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_invalid_formcast_index[period] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend[period] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality[period] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier[period] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_time_trend_smoke[False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_fourier_smoke[False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[False-freq_period0] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[False-freq_period1] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[False-freq_period2] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[False-freq_period3] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[False-freq_period4] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[False-freq_period5] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[False-freq_period6] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[False-freq_period7] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[False-freq_period8] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[False-freq_period9] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[False-freq_period10] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[False-freq_period11] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[False-freq_period12] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[False-freq_period13] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[False-freq_period14] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_invalid_formcast_index[range] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_time_trend[range] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality[range] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_fourier[range] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_time_trend_smoke[None] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_fourier_smoke[None] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[None-freq_period0] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[None-freq_period1] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[None-freq_period2] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[None-freq_period3] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[None-freq_period4] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[None-freq_period5] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[None-freq_period6] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[None-freq_period7] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[None-freq_period8] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[None-freq_period9] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[None-freq_period10] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[None-freq_period11] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[None-freq_period12] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[None-freq_period13] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonality[None-freq_period14] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_forbidden_index PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_time_trend_base PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_invalid_freq_period PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_check_index_type PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_unknown_freq PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonal_from_index_err PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_seasonality_time_index PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_index_like PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_fourier PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_time_trend PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonal_period_w PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonal_period_d PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonal_period_q PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_calendar_seasonal_period_a PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-0-True-0-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-0-True-0-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-0-True-1-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-0-True-1-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-0-False-0-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-0-False-0-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-0-False-1-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-0-False-1-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-1-True-0-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-1-True-0-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-1-True-1-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-1-True-1-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-1-False-0-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-1-False-0-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-1-False-1-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-None-1-False-1-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-0-True-0-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-0-True-0-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-0-True-1-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-0-True-1-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-0-False-0-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-0-False-0-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-0-False-1-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-0-False-1-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-1-True-0-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-1-True-0-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-1-True-1-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-1-True-1-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-1-False-0-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-1-False-0-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-1-False-1-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[True-10-1-False-1-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-0-True-0-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-0-True-0-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-0-True-1-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-0-True-1-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-0-False-0-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-0-False-0-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-0-False-1-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-0-False-1-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-1-True-0-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-1-True-0-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-1-True-1-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-1-True-1-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-1-False-0-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-1-False-0-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-1-False-1-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-None-1-False-1-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-0-True-0-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-0-True-0-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-0-True-1-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-0-True-1-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-0-False-0-True] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-0-False-0-False] PASSED [ 95%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-0-False-1-True] PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-0-False-1-False] PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-1-True-0-True] PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-1-True-0-False] PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-1-True-1-True] PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-1-True-1-False] PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-1-False-0-True] PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-1-False-0-False] PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-1-False-1-True] PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process[False-10-1-False-1-False] PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_deterministic_process_errors PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_range_error PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_range_index_basic PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_range_casting PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_non_unit_range PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_additional_terms PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_drop_two_consants PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_determintic_term_equiv[index0] PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_determintic_term_equiv[index1] PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_determintic_term_equiv[index2] PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_determintic_term_equiv[index3] PASSED [ 96%] statsmodels/tsa/tests/test_deterministic.py::test_drop PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[AAdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[AAN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[AMdN] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[AMN] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[ANN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[MAdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[MAN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[MMdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[MMN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[MNN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[AAdA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[AAA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[AAdM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[AAM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[AMdA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[AMA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[AMdM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[AMM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[ANA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[ANM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[MAdA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[MAA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[MAdM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[MAM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[MMdA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[MMA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[MMdM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[MMM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[MNA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_model_austouritsts[MNM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[AAdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[AAN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[AMdN] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[AMN] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[ANN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[MAdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[MAN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[MMdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[MMN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[MNN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[AAdA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[AAA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[AAdM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[AAM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[AMdA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[AMA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[AMdM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[AMM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[ANA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[ANM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[MAdA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[MAA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[MAdM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[MAM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[MMdA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[MMA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[MMdM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[MMM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[MNA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_smooth_vs_R[MNM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[AAdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[AAN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[AMdN] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[AMN] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[ANN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[MAdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[MAN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[MMdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[MMN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[MNN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[AAdA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[AAA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[AAdM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[AAM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[AMdA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[AMA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[AMdM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[AMM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[ANA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[ANM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[MAdA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[MAA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[MAdM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[MAM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[MMdA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[MMA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[MMdM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[MMM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[MNA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_residuals_vs_R[MNM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[AAdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[AAN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[AMdN] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[AMN] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[ANN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[MAdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[MAN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[MMdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[MMN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[MNN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[AAdA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[AAA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[AAdM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[AAM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[AMdA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[AMA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[AMdM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[AMM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[ANA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[ANM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[MAdA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[MAA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[MAdM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[MAM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[MMdA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[MMA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[MMdM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[MMM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[MNA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_loglike_vs_R[MNM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[AAdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[AAN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[AMdN] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[AMN] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[ANN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[MAdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[MAN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[MMdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[MMN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[MNN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[AAdA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[AAA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[AAdM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[AAM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[AMdA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[AMA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[AMdM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[AMM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[ANA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[ANM] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[MAdA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[MAA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[MAdM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[MAM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[MMdA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[MMA] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[MMdM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[MMM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[MNA] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_forecast_vs_R[MNM] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[AAdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[AAN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[AMdN] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[AMN] SKIPPED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[ANN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[MAdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[MAN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[MMdN] PASSED [ 96%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[MMN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[MNN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[AAdA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[AAA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[AAdM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[AAM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[AMdA] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[AMA] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[AMdM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[AMM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[ANA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[ANM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[MAdA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[MAA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[MAdM] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[MAM] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[MMdA] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[MMA] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[MMdM] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[MMM] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[MNA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_vs_R[MNM] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[AAdN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[AAN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[AMdN] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[AMN] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[ANN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[MAdN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[MAN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[MMdN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[MMN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[MNN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[AAdA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[AAA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[AAdM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[AAM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[AMdA] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[AMA] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[AMdM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[AMM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[ANA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[ANM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[MAdA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[MAA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[MAdM] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[MAM] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[MMdA] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[MMA] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[MMdM] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[MMM] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[MNA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_fit_vs_R[MNM] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[AAdN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[AAN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[AMdN] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[AMN] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[ANN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[MAdN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[MAN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[MMdN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[MMN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[MNN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[AAdA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[AAA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[AAdM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[AAM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[AMdA] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[AMA] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[AMdM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[AMM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[ANA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[ANM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[MAdA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[MAA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[MAdM] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[MAM] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[MMdA] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[MMA] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[MMdM] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[MMM] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[MNA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_vs_R[MNM] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_initialization_known PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_initialization_heuristic PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_bounded_fit PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_seasonal_periods PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_simulate_keywords PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_predict_ranges PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_summary PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_score PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_hessian PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_prediction_results PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_results_vs_statespace PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_prediction_results_vs_statespace PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_prediction_results_slow_AAN SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_prediction_results_slow_AAdA SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_convergence_simple PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_exact_prediction_intervals PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[AAdN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[AAN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[AMdN] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[AMN] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[ANN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[MAdN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[MAN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[MMdN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[MMN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[MNN] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[AAdA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[AAA] PASSED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[AAdM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[AAM] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[AMdA] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[AMA] SKIPPED [ 97%] statsmodels/tsa/tests/test_exponential_smoothing.py::test_one_step_ahead[AMdM] SKIPPED [ 97%] 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statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags[False-False-True-raise] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags[False-False-True-none] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[True-True-False-conservative] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[True-True-False-drop] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[True-True-True-conservative] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[True-True-True-drop] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[True-False-False-conservative] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[True-False-False-drop] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[True-False-True-conservative] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[True-False-True-drop] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[False-True-False-conservative] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[False-True-False-drop] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[False-True-True-conservative] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[False-True-True-drop] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[False-False-False-conservative] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[False-False-False-drop] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[False-False-True-conservative] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_nlags_missing[False-False-True-drop] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acovf_error PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_pacf2acf_ar PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_pacf2acf_levinson_durbin PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_pacf2acf_errors PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_pacf_burg PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_pacf_burg_error PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_innovations_algo_brockwell_davis PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_innovations_algo_rtol PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_innovations_errors PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_innovations_filter_brockwell_davis PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_innovations_filter_pandas PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_innovations_filter_errors PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_innovations_algo_filter_kalman_filter PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_adfuller_short_series PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_adfuller_maxlag_too_large PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::TestZivotAndrews::test_fail_regression_type PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::TestZivotAndrews::test_fail_trim_value PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::TestZivotAndrews::test_fail_array_shape PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::TestZivotAndrews::test_fail_autolag_type PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::TestZivotAndrews::test_autolag_case_sensitivity[AIC] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::TestZivotAndrews::test_autolag_case_sensitivity[aic] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::TestZivotAndrews::test_autolag_case_sensitivity[Aic] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::TestZivotAndrews::test_rgnp_case PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::TestZivotAndrews::test_gnpdef_case PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::TestZivotAndrews::test_stkprc_case PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::TestZivotAndrews::test_rgnpq_case PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::TestZivotAndrews::test_rand10000_case PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_acf_conservate_nanops PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_pacf_nlags_error PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_coint_auto_tstat PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_granger_causality_exceptions[dataset0] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_granger_causality_exceptions[dataset1] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_granger_causality_exceptions[dataset2] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_granger_causality_exceptions[dataset3] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_granger_causality_exception_maxlag PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_granger_causality_verbose PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_pacf_small_sample[3] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_pacf_small_sample[5] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_pacf_small_sample[7] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_pacf_small_sample[9] PASSED [ 98%] statsmodels/tsa/tests/test_stattools.py::test_pacf_1_obs PASSED [ 98%] statsmodels/tsa/tests/test_tsa_tools.py::test_acf PASSED [ 98%] statsmodels/tsa/tests/test_tsa_tools.py::test_ccf PASSED [ 98%] statsmodels/tsa/tests/test_tsa_tools.py::test_pacf_yw PASSED [ 98%] statsmodels/tsa/tests/test_tsa_tools.py::test_pacf_ols PASSED [ 98%] statsmodels/tsa/tests/test_tsa_tools.py::test_ywcoef PASSED [ 98%] statsmodels/tsa/tests/test_tsa_tools.py::test_yule_walker_inter PASSED [ 98%] statsmodels/tsa/tests/test_tsa_tools.py::test_duplication_matrix PASSED [ 98%] statsmodels/tsa/tests/test_tsa_tools.py::test_elimination_matrix PASSED [ 98%] statsmodels/tsa/tests/test_tsa_tools.py::test_commutation_matrix PASSED [ 98%] statsmodels/tsa/tests/test_tsa_tools.py::test_vec PASSED [ 98%] statsmodels/tsa/tests/test_tsa_tools.py::test_vech PASSED [ 98%] statsmodels/tsa/tests/test_tsa_tools.py::test_ar_transparams PASSED [ 98%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_add_lag_insert PASSED [ 98%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_add_lag_noinsert PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_add_lag_noinsert_atend PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_add_lag_ndarray PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_add_lag_noinsert_ndarray PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_add_lag_noinsertatend_ndarray PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_sep_return PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_add_lag1d PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_add_lag1d_drop PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_add_lag1d_struct PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_add_lag_1d_drop_struct PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_add_lag_drop_insert PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_add_lag_drop_noinsert PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_dataframe_without_pandas PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_dataframe_both PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_too_few_observations PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_unknown_trim PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_dataframe_forward PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_pandas_errors PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_series_forward PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_series_both PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_range_index_columns PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat::test_duplicate_column_names PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::test_freq_to_period[freq_expected0] PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::test_freq_to_period[freq_expected1] PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::test_freq_to_period[freq_expected2] PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::test_freq_to_period[freq_expected3] PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::test_freq_to_period[freq_expected4] PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::test_freq_to_period[freq_expected5] PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::test_freq_to_period[freq_expected6] PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::test_freq_to_period[freq_expected7] PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::test_freq_to_period[freq_expected8] PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::test_freq_to_period[freq_expected9] PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestDetrend::test_detrend_1d PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestDetrend::test_detrend_2d PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestDetrend::test_detrend_series PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestDetrend::test_detrend_dataframe PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestDetrend::test_detrend_dim_too_large PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestAddTrend::test_series PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestAddTrend::test_dataframe PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestAddTrend::test_duplicate_const PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestAddTrend::test_dataframe_duplicate PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestAddTrend::test_array PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestAddTrend::test_unknown_trend PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestAddTrend::test_trend_n PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat2DS::test_lagmat2ds_numpy PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat2DS::test_lagmat2ds_pandas PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat2DS::test_lagmat2ds_use_pandas PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::TestLagmat2DS::test_3d_error PASSED [ 99%] statsmodels/tsa/tests/test_tsa_tools.py::test_grangercausality PASSED [ 99%] statsmodels/tsa/tests/test_x13.py::test_x13_arima_select_order[monthly] SKIPPED [ 99%] statsmodels/tsa/tests/test_x13.py::test_x13_arima_select_order[monthly_start] SKIPPED [ 99%] statsmodels/tsa/tests/test_x13.py::test_x13_arima_select_order[monthly_co2] SKIPPED [ 99%] statsmodels/tsa/tests/test_x13.py::test_x13_arima_select_order[monthly_start_co2] SKIPPED [ 99%] statsmodels/tsa/tests/test_x13.py::test_x13_arima_select_order[series] SKIPPED [ 99%] statsmodels/tsa/tests/test_x13.py::test_x13_arima_select_order[dataframe] SKIPPED [ 99%] statsmodels/tsa/tests/test_x13.py::test_x13_arima_plot[monthly] SKIPPED [ 99%] statsmodels/tsa/tests/test_x13.py::test_x13_arima_plot[monthly_start] SKIPPED [ 99%] statsmodels/tsa/tests/test_x13.py::test_x13_arima_plot[monthly_co2] SKIPPED [ 99%] statsmodels/tsa/tests/test_x13.py::test_x13_arima_plot[monthly_start_co2] SKIPPED [ 99%] statsmodels/tsa/tests/test_x13.py::test_x13_arima_plot[series] SKIPPED [ 99%] statsmodels/tsa/tests/test_x13.py::test_x13_arima_plot[dataframe] SKIPPED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJoh12::test_basic PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJoh12::test_table_trace PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJoh12::test_table_maxeval PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJoh12::test_normalization PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJoh12::test_evec PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJoh12::test_evals PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJoh09::test_basic PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJoh09::test_table_trace PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJoh09::test_table_maxeval PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJoh09::test_normalization PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJohMin18::test_basic PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJohMin18::test_table_trace PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJohMin18::test_table_maxeval PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJohMin18::test_normalization PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJoh25::test_basic PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJoh25::test_table_trace PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJoh25::test_table_maxeval PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::TestCointJoh25::test_normalization PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_coint.py::test_coint_johansen_0lag PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_svar.py::TestSVAR::test_A PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_svar.py::TestSVAR::test_B PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_svar.py::TestSVAR::test_basic PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_svar.py::TestSVAR::test_llf_ic PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_svar.py::TestSVAR::test_irf PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_fevd_plot PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_fevd_repr PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_fevd_summary PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_fevd_cov XFAIL [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_irf_coefs PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_plot_irf PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_plot_cum_effects PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_plot_figsizes PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_constructor PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_names PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_get_eq_index PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_repr PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_params PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_cov_params PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_cov_ybar PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_tstat PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_pvalues PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_summary PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_detsig PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_aic PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_bic PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_hqic PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_fpe PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_lagorder_select PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_nobs PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_stderr PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_loglike PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_ma_rep PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_causality PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_causality_no_lags PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_select_order PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_is_stable PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_acf PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_acf_2_lags PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_acorr PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_forecast PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_forecast_interval PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_plot_sim PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_var.py::TestVARResults::test_plot PASSED [ 99%] 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statsmodels/tsa/vector_ar/tests/test_vecm.py::test_impulse_response PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_vecm.py::test_lag_order_selection PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_vecm.py::test_normality PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_vecm.py::test_whiteness PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_vecm.py::test_summary PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_vecm.py::test_exceptions PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_vecm.py::test_select_coint_rank PASSED [ 99%] statsmodels/tsa/vector_ar/tests/test_vecm.py::test_VECM_seasonal_forecast PASSED [100%] ==================================== ERRORS ==================================== ____ ERROR at setup of TestZeroInflatedGeneralizedPoisson_predict.test_mean ____ cond = array([False, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20.... 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80.]), 2.0, 0.5, 2, 0.5) f1 = . at 0x3f7f531fe0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: cls = @classmethod def setup_class(cls): expected_params = [1, 0.5, 0.5] np.random.seed(999) nobs = 2000 exog = np.ones((nobs, 2)) exog[:nobs//2, 1] = 2 mu_true = exog.dot(expected_params[:-1]) > cls.endog = sm.distributions.zigenpoisson.rvs(mu_true, expected_params[-1], 2, 0.5, size=mu_true.shape) statsmodels/discrete/tests/test_count_model.py:377: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3504: in rvs return super().rvs(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:1132: in rvs vals = self._rvs(*args, size=size, random_state=random_state) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:1058: in _rvs Y = self._ppf(U, *args) ^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:1073: in _ppf return self._ppfvec(q, *args) ^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2529: in __call__ return self._call_as_normal(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2522: in _call_as_normal return self._vectorize_call(func=func, args=vargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2610: in _vectorize_call outputs = ufunc(*args, out=...) ^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3111: in _drv2_ppfsingle qb = self._cdf(b, *args) ^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3470: in _cdf return self._cdfvec(k, *args) ^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2529: in __call__ return self._call_as_normal(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2522: in _call_as_normal return self._vectorize_call(func=func, args=vargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2610: in _vectorize_call outputs = ufunc(*args, out=...) ^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3466: in _cdf_single return np.sum(self._pmf(m, *args), axis=0) ^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:106: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:95: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([False, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20.... 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80.]), 2.0, 0.5, 2, 0.5) f1 = . at 0x3f7f531fe0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ____ ERROR at setup of TestZeroInflatedGeneralizedPoisson_predict.test_var _____ cond = array([False, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20.... 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80.]), 2.0, 0.5, 2, 0.5) f1 = . at 0x3f7f531fe0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: cls = @classmethod def setup_class(cls): expected_params = [1, 0.5, 0.5] np.random.seed(999) nobs = 2000 exog = np.ones((nobs, 2)) exog[:nobs//2, 1] = 2 mu_true = exog.dot(expected_params[:-1]) > cls.endog = sm.distributions.zigenpoisson.rvs(mu_true, expected_params[-1], 2, 0.5, size=mu_true.shape) statsmodels/discrete/tests/test_count_model.py:377: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3504: in rvs return super().rvs(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:1132: in rvs vals = self._rvs(*args, size=size, random_state=random_state) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:1058: in _rvs Y = self._ppf(U, *args) ^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:1073: in _ppf return self._ppfvec(q, *args) ^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2529: in __call__ return self._call_as_normal(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2522: in _call_as_normal return self._vectorize_call(func=func, args=vargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2610: in _vectorize_call outputs = ufunc(*args, out=...) ^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3111: in _drv2_ppfsingle qb = self._cdf(b, *args) ^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3470: in _cdf return self._cdfvec(k, *args) ^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2529: in __call__ return self._call_as_normal(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2522: in _call_as_normal return self._vectorize_call(func=func, args=vargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2610: in _vectorize_call outputs = ufunc(*args, out=...) ^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3466: in _cdf_single return np.sum(self._pmf(m, *args), axis=0) ^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:106: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:95: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([False, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20.... 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80.]), 2.0, 0.5, 2, 0.5) f1 = . at 0x3f7f531fe0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError _ ERROR at setup of TestZeroInflatedGeneralizedPoisson_predict.test_predict_prob _ cond = array([False, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20.... 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80.]), 2.0, 0.5, 2, 0.5) f1 = . at 0x3f7f531fe0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: cls = @classmethod def setup_class(cls): expected_params = [1, 0.5, 0.5] np.random.seed(999) nobs = 2000 exog = np.ones((nobs, 2)) exog[:nobs//2, 1] = 2 mu_true = exog.dot(expected_params[:-1]) > cls.endog = sm.distributions.zigenpoisson.rvs(mu_true, expected_params[-1], 2, 0.5, size=mu_true.shape) statsmodels/discrete/tests/test_count_model.py:377: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3504: in rvs return super().rvs(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:1132: in rvs vals = self._rvs(*args, size=size, random_state=random_state) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:1058: in _rvs Y = self._ppf(U, *args) ^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:1073: in _ppf return self._ppfvec(q, *args) ^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2529: in __call__ return self._call_as_normal(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2522: in _call_as_normal return self._vectorize_call(func=func, args=vargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2610: in _vectorize_call outputs = ufunc(*args, out=...) ^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3111: in _drv2_ppfsingle qb = self._cdf(b, *args) ^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3470: in _cdf return self._cdfvec(k, *args) ^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2529: in __call__ return self._call_as_normal(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2522: in _call_as_normal return self._vectorize_call(func=func, args=vargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2610: in _vectorize_call outputs = ufunc(*args, out=...) ^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3466: in _cdf_single return np.sum(self._pmf(m, *args), axis=0) ^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:106: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:95: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([False, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20.... 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80.]), 2.0, 0.5, 2, 0.5) f1 = . at 0x3f7f531fe0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError =================================== FAILURES =================================== ______________ TestZeroInflatedPoisson_predict.test_predict_prob _______________ cond = array([False, True, True, ..., True, True, True], shape=(20000,)) args = (array([0., 1., 2., ..., 7., 8., 9.], shape=(20000,)), array([2.02731222, 2.02731222, 2.02731222, ..., 1.43852146, 1.4...(20000,)), array([0.05226733, 0.05226733, 0.05226733, ..., 0.05226733, 0.05226733, 0.05226733], shape=(20000,))) f1 = . at 0x3f841d64b0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_predict_prob(self): res = self.res  > pr = res.predict(which='prob') ^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/discrete/tests/test_count_model.py:278: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/base/model.py:1174: in predict predict_results = self.model.predict(self.params, exog, *args, statsmodels/discrete/count_model.py:500: in predict return self._predict_prob(params, exog, exog_infl, exposure, statsmodels/discrete/count_model.py:662: in _predict_prob result = self.distribution.pmf(y_values, mu, w) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:56: in _pmf return np.exp(self._logpmf(x, mu, w)) ^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:48: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([False, True, True, ..., True, True, True], shape=(20000,)) args = (array([0., 1., 2., ..., 7., 8., 9.], shape=(20000,)), array([2.02731222, 2.02731222, 2.02731222, ..., 1.43852146, 1.4...(20000,)), array([0.05226733, 0.05226733, 0.05226733, ..., 0.05226733, 0.05226733, 0.05226733], shape=(20000,))) f1 = . at 0x3f841d64b0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError _____________ TestZeroInflatedPoisson_predict.test_predict_options _____________ cond = array([False, True, True, ..., True, True, True], shape=(20000,)) args = (array([0., 1., 2., ..., 7., 8., 9.], shape=(20000,)), array([2.02731222, 2.02731222, 2.02731222, ..., 1.43852146, 1.4...(20000,)), array([0.05226733, 0.05226733, 0.05226733, ..., 0.05226733, 0.05226733, 0.05226733], shape=(20000,))) f1 = . at 0x3f841d5900> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_predict_options(self): # check default exog_infl, see #4757 res = self.res n = 5 > pr1 = res.predict(which='prob') ^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/discrete/tests/test_count_model.py:287: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/base/model.py:1174: in predict predict_results = self.model.predict(self.params, exog, *args, statsmodels/discrete/count_model.py:500: in predict return self._predict_prob(params, exog, exog_infl, exposure, statsmodels/discrete/count_model.py:662: in _predict_prob result = self.distribution.pmf(y_values, mu, w) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:56: in _pmf return np.exp(self._logpmf(x, mu, w)) ^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:48: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([False, True, True, ..., True, True, True], shape=(20000,)) args = (array([0., 1., 2., ..., 7., 8., 9.], shape=(20000,)), array([2.02731222, 2.02731222, 2.02731222, ..., 1.43852146, 1.4...(20000,)), array([0.05226733, 0.05226733, 0.05226733, ..., 0.05226733, 0.05226733, 0.05226733], shape=(20000,))) f1 = . at 0x3f841d5900> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError _________ TestZeroInflatedNegativeBinomialP_predict.test_predict_prob __________ cond = array([False, True, True, ..., True, True, True], shape=(190000,)) args = (array([ 0., 1., 2., ..., 35., 36., 37.], shape=(190000,)), array([1.996657, 1.996657, 1.996657, ..., 1.996657, 1.99...90000,)), array([0.15577043, 0.15577043, 0.15577043, ..., 0.15577043, 0.15577043, 0.15577043], shape=(190000,))) f1 = . at 0x3f84b68250> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_predict_prob(self): res = self.res endog = res.model.endog  > pr = res.predict(which='prob') ^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/discrete/tests/test_count_model.py:567: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/base/model.py:1174: in predict predict_results = self.model.predict(self.params, exog, *args, statsmodels/discrete/count_model.py:500: in predict return self._predict_prob(params, exog, exog_infl, exposure, statsmodels/discrete/count_model.py:920: in _predict_prob result = self.distribution.pmf(y_values, mu, params_main[-1], p, w) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:139: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:131: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([False, True, True, ..., True, True, True], shape=(190000,)) args = (array([ 0., 1., 2., ..., 35., 36., 37.], shape=(190000,)), array([1.996657, 1.996657, 1.996657, ..., 1.996657, 1.99...90000,)), array([0.15577043, 0.15577043, 0.15577043, ..., 0.15577043, 0.15577043, 0.15577043], shape=(190000,))) f1 = . at 0x3f84b68250> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ______ TestZeroInflatedNegativeBinomialP_predict.test_predict_generic_zi _______ cond = array([False, True, True, ..., True, True, True], shape=(190000,)) args = (array([ 0., 1., 2., ..., 35., 36., 37.], shape=(190000,)), array([1.996657, 1.996657, 1.996657, ..., 1.996657, 1.99...90000,)), array([0.15577043, 0.15577043, 0.15577043, ..., 0.15577043, 0.15577043, 0.15577043], shape=(190000,))) f1 = . at 0x3f84b5eda0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_predict_generic_zi(self): # These tests do not use numbers from other packages. # Tests are on closeness of estimated to true/DGP values # and theoretical relationship between quantities res = self.res endog = self.endog exog = self.res.model.exog prob_infl = self.prob_infl nobs = len(endog)  freq = np.bincount(endog.astype(int)) / len(endog) > probs = res.predict(which='prob') ^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/discrete/tests/test_count_model.py:588: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/base/model.py:1174: in predict predict_results = self.model.predict(self.params, exog, *args, statsmodels/discrete/count_model.py:500: in predict return self._predict_prob(params, exog, exog_infl, exposure, statsmodels/discrete/count_model.py:920: in _predict_prob result = self.distribution.pmf(y_values, mu, params_main[-1], p, w) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:139: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:131: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([False, True, True, ..., True, True, True], shape=(190000,)) args = (array([ 0., 1., 2., ..., 35., 36., 37.], shape=(190000,)), array([1.996657, 1.996657, 1.996657, ..., 1.996657, 1.99...90000,)), array([0.15577043, 0.15577043, 0.15577043, ..., 0.15577043, 0.15577043, 0.15577043], shape=(190000,))) f1 = . at 0x3f84b5eda0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError _________________ TestZINegativeBinomialPPredict.test_predict __________________ cond = array([False, True]) args = (array([0, 1]), array([1.62353219, 1.62353219]), array([0.20851703, 0.20851703]), array([0.00012919, 0.00012919])) f1 = . at 0x3f840b6560> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_predict(self): res1 = self.res1 res2 = self.res2 ex = np.asarray(exog).mean(0)  # test for which="mean" rdf = res2.results_margins_atmeans pred = res1.get_prediction(ex, **self.pred_kwds_mean) assert_allclose(pred.predicted, rdf["b"].iloc[0], rtol=1e-4) assert_allclose(pred.se, rdf["se"].iloc[0], rtol=1e-4, atol=1e-4) if isinstance(pred, PredictionResultsMonotonic): # default method is endpoint transformation for non-ZI models ci = pred.conf_int()[0] assert_allclose(ci[0], rdf["ll"].iloc[0], rtol=1e-3, atol=1e-4) assert_allclose(ci[1], rdf["ul"].iloc[0], rtol=1e-3, atol=1e-4)  ci = pred.conf_int(method="delta")[0] assert_allclose(ci[0], rdf["ll"].iloc[0], rtol=1e-4, atol=1e-4) assert_allclose(ci[1], rdf["ul"].iloc[0], rtol=1e-4, atol=1e-4) else: ci = pred.conf_int()[0] assert_allclose(ci[0], rdf["ll"].iloc[0], rtol=1e-4, atol=1e-4) assert_allclose(ci[1], rdf["ul"].iloc[0], rtol=1e-4, atol=1e-4)  stat, _ = pred.t_test() assert_allclose(stat, pred.tvalues, rtol=1e-4, atol=1e-4)  rdf = res2.results_margins_mean pred = res1.get_prediction(average=True, **self.pred_kwds_mean) assert_allclose(pred.predicted, rdf["b"].iloc[0], rtol=3e-4) # self.rtol) assert_allclose(pred.se, rdf["se"].iloc[0], rtol=3e-3, atol=1e-4) if isinstance(pred, PredictionResultsMonotonic): # default method is endpoint transformation for non-ZI models ci = pred.conf_int()[0] assert_allclose(ci[0], rdf["ll"].iloc[0], rtol=1e-3, atol=1e-4) assert_allclose(ci[1], rdf["ul"].iloc[0], rtol=1e-3, atol=1e-4)  ci = pred.conf_int(method="delta")[0] assert_allclose(ci[0], rdf["ll"].iloc[0], rtol=1e-4, atol=1e-4) assert_allclose(ci[1], rdf["ul"].iloc[0], rtol=1e-4, atol=1e-4) else: ci = pred.conf_int()[0] assert_allclose(ci[0], rdf["ll"].iloc[0], rtol=5e-4, atol=1e-4) assert_allclose(ci[1], rdf["ul"].iloc[0], rtol=5e-4, atol=1e-4)  stat, _ = pred.t_test() assert_allclose(stat, pred.tvalues, rtol=1e-4, atol=1e-4)  # test for which="prob" rdf = res2.results_margins_atmeans > pred = res1.get_prediction(ex, which="prob", y_values=np.arange(2), **self.pred_kwds_mean) statsmodels/discrete/tests/test_predict.py:109: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/discrete/count_model.py:984: in get_prediction res = pred.get_prediction_delta(self, exog=exog, which=which, statsmodels/base/_prediction_inference.py:693: in get_prediction_delta res = PredictionResultsDelta(nlpm) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/base/_prediction_inference.py:233: in __init__ predicted = results_delta.predicted() ^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_delta_method.py:109: in predicted predicted = self.fun(self.params) ^^^^^^^^^^^^^^^^^^^^^ statsmodels/base/_prediction_inference.py:685: in f_pred pred = self.model.predict(p, exog, which=which, **pred_kwds) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/discrete/count_model.py:500: in predict return self._predict_prob(params, exog, exog_infl, exposure, statsmodels/discrete/count_model.py:920: in _predict_prob result = self.distribution.pmf(y_values, mu, params_main[-1], p, w) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:139: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:131: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([False, True]) args = (array([0, 1]), array([1.62353219, 1.62353219]), array([0.20851703, 0.20851703]), array([0.00012919, 0.00012919])) f1 = . at 0x3f840b6560> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ________________ TestZINegativeBinomialPPredict.test_diagnostic ________________ cond = array([False, True, True, ..., False, True, True], shape=(10887,)) args = (array([0, 1, 2, ..., 0, 1, 2], shape=(10887,)), array([1.62353219, 1.62353219, 1.62353219, ..., 1.62353219, 1.6235321...9893e-06, 8.14429893e-06, 8.14429893e-06, ..., 1.38007162e-03, 1.38007162e-03, 1.38007162e-03], shape=(10887,))) f1 = . at 0x3f7f4df690> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_diagnostic(self): # smoke test for now res1 = self.res1  dia = res1.get_diagnostic(y_max=21) > res_chi2 = dia.test_chisquare_prob(bin_edges=np.arange(4)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/discrete/tests/test_predict.py:154: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/discrete/diagnostic.py:109: in test_chisquare_prob probs = self.results.predict(which="prob", **kwds) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/base/model.py:1174: in predict predict_results = self.model.predict(self.params, exog, *args, statsmodels/discrete/count_model.py:500: in predict return self._predict_prob(params, exog, exog_infl, exposure, statsmodels/discrete/count_model.py:920: in _predict_prob result = self.distribution.pmf(y_values, mu, params_main[-1], p, w) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:139: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:131: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([False, True, True, ..., False, True, True], shape=(10887,)) args = (array([0, 1, 2, ..., 0, 1, 2], shape=(10887,)), array([1.62353219, 1.62353219, 1.62353219, ..., 1.62353219, 1.6235321...9893e-06, 8.14429893e-06, 8.14429893e-06, ..., 1.38007162e-03, 1.38007162e-03, 1.38007162e-03], shape=(10887,))) f1 = . at 0x3f7f4df690> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ______________________________ test_distr[case2] _______________________________ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...2, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552])) f1 = . at 0x3f7f58c720> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: case = (, {}, array([-1.5 , 0.40546511])) @pytest.mark.parametrize("case", models) def test_distr(case): y, x = y_count, x_const nobs = len(y) np.random.seed(987456348)  cls_model, kwds, params = case if issubclass(cls_model, BinaryModel): y = (y > 0.5).astype(float)  mod = cls_model(y, x, **kwds) # res = mod.fit() params_dgp = params distr = mod.get_distribution(params_dgp) > assert distr.pmf(1).ndim == 1 ^^^^^^^^^^^^ statsmodels/discrete/tests/test_predict.py:362: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:598: in pmf return self.dist.pmf(k, *self.args, **self.kwds) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:56: in _pmf return np.exp(self._logpmf(x, mu, w)) ^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:48: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...2, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552])) f1 = . at 0x3f7f58c720> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ______________________________ test_distr[case3] _______________________________ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...2, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552])) f1 = . at 0x3f840e71c0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: case = (, {}, array([-1.5 , 0.40546511, 1.5 ])) @pytest.mark.parametrize("case", models) def test_distr(case): y, x = y_count, x_const nobs = len(y) np.random.seed(987456348)  cls_model, kwds, params = case if issubclass(cls_model, BinaryModel): y = (y > 0.5).astype(float)  mod = cls_model(y, x, **kwds) # res = mod.fit() params_dgp = params distr = mod.get_distribution(params_dgp) > assert distr.pmf(1).ndim == 1 ^^^^^^^^^^^^ statsmodels/discrete/tests/test_predict.py:362: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:598: in pmf return self.dist.pmf(k, *self.args, **self.kwds) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:106: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:95: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...2, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552])) f1 = . at 0x3f840e71c0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ______________________________ test_distr[case4] _______________________________ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...2, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552])) f1 = . at 0x3f7f58c930> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: case = (, {'p': 1}, array([-1.5 , 0.40546511, 1.5 ])) @pytest.mark.parametrize("case", models) def test_distr(case): y, x = y_count, x_const nobs = len(y) np.random.seed(987456348)  cls_model, kwds, params = case if issubclass(cls_model, BinaryModel): y = (y > 0.5).astype(float)  mod = cls_model(y, x, **kwds) # res = mod.fit() params_dgp = params distr = mod.get_distribution(params_dgp) > assert distr.pmf(1).ndim == 1 ^^^^^^^^^^^^ statsmodels/discrete/tests/test_predict.py:362: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:598: in pmf return self.dist.pmf(k, *self.args, **self.kwds) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:106: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:95: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...2, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552])) f1 = . at 0x3f7f58c930> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ______________________________ test_distr[case5] _______________________________ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...2, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552])) f1 = . at 0x3f7f58d7a0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: case = (, {}, array([-1.5 , 0.40546511, 1.5 ])) @pytest.mark.parametrize("case", models) def test_distr(case): y, x = y_count, x_const nobs = len(y) np.random.seed(987456348)  cls_model, kwds, params = case if issubclass(cls_model, BinaryModel): y = (y > 0.5).astype(float)  mod = cls_model(y, x, **kwds) # res = mod.fit() params_dgp = params distr = mod.get_distribution(params_dgp) > assert distr.pmf(1).ndim == 1 ^^^^^^^^^^^^ statsmodels/discrete/tests/test_predict.py:362: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:598: in pmf return self.dist.pmf(k, *self.args, **self.kwds) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:139: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:131: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...2, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552])) f1 = . at 0x3f7f58d7a0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ______________________________ test_distr[case6] _______________________________ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...2, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552])) f1 = . at 0x3f7f58d380> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: case = (, {'p': 1}, array([-1.5 , 0.40546511, 1.5 ])) @pytest.mark.parametrize("case", models) def test_distr(case): y, x = y_count, x_const nobs = len(y) np.random.seed(987456348)  cls_model, kwds, params = case if issubclass(cls_model, BinaryModel): y = (y > 0.5).astype(float)  mod = cls_model(y, x, **kwds) # res = mod.fit() params_dgp = params distr = mod.get_distribution(params_dgp) > assert distr.pmf(1).ndim == 1 ^^^^^^^^^^^^ statsmodels/discrete/tests/test_predict.py:362: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:598: in pmf return self.dist.pmf(k, *self.args, **self.kwds) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:139: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:131: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...2, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552, 0.18242552])) f1 = . at 0x3f7f58d380> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError _________________________ TestZIPoisson.test_pmf_zero __________________________ cond = array([ True]), args = (array([3]), array([2]), array([0])) f1 = . at 0x3f7fd50250> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_pmf_zero(self): poisson_pmf = poisson.pmf(3, 2) > zipoisson_pmf = zipoisson.pmf(3, 2, 0) ^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/tests/test_discrete.py:80: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:56: in _pmf return np.exp(self._logpmf(x, mu, w)) ^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:48: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True]), args = (array([3]), array([2]), array([0])) f1 = . at 0x3f7fd50250> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ________________________ TestZIPoisson.test_logpmf_zero ________________________ cond = array([ True]), args = (array([5]), array([1]), array([0])) f1 = . at 0x3f7fd50880> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_logpmf_zero(self): poisson_logpmf = poisson.logpmf(5, 1) > zipoisson_logpmf = zipoisson.logpmf(5, 1, 0) ^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/tests/test_discrete.py:85: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3578: in logpmf place(output, cond, self._logpmf(*goodargs)) ^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:48: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True]), args = (array([5]), array([1]), array([0])) f1 = . at 0x3f7fd50880> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ____________________________ TestZIPoisson.test_pmf ____________________________ cond = array([ True]), args = (array([2]), array([2]), array([0.1])) f1 = . at 0x3f7fd50bf0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_pmf(self): poisson_pmf = poisson.pmf(2, 2) > zipoisson_pmf = zipoisson.pmf(2, 2, 0.1) ^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/tests/test_discrete.py:90: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:56: in _pmf return np.exp(self._logpmf(x, mu, w)) ^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:48: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True]), args = (array([2]), array([2]), array([0.1])) f1 = . at 0x3f7fd50bf0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError __________________________ TestZIPoisson.test_logpmf ___________________________ cond = array([ True]), args = (array([7]), array([3]), array([0.1])) f1 = . at 0x3f7fd50a90> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_logpmf(self): poisson_logpmf = poisson.logpmf(7, 3) > zipoisson_logpmf = zipoisson.logpmf(7, 3, 0.1) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/tests/test_discrete.py:95: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3578: in logpmf place(output, cond, self._logpmf(*goodargs)) ^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:48: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True]), args = (array([7]), array([3]), array([0.1])) f1 = . at 0x3f7fd50a90> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError __________________________ TestZIPoisson.test_moments __________________________ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43.]), 12, 0) f1 = . at 0x3f7fd51dd0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_moments(self): poisson_m1, poisson_m2 = poisson.moment(1, 12), poisson.moment(2, 12) zip_m0 = zipoisson.moment(0, 12, 0) > zip_m1 = zipoisson.moment(1, 12, 0) ^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/tests/test_discrete.py:125: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3973: in moment return super().moment(order, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:1339: in moment val[...] = _moment_from_stats(n, mu, mu2, g1, g2, self._munp, shapes) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:384: in _moment_from_stats val = moment_func(1, *args) ^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:929: in _munp vals = self.generic_moment(n, *args) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2529: in __call__ return self._call_as_normal(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2522: in _call_as_normal return self._vectorize_call(func=func, args=vargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2610: in _vectorize_call outputs = ufunc(*args, out=...) ^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3096: in _drv2_moment return _expect(fun, _a, _b, self._ppf(0.5, *args), self.inc) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:4019: in _expect delta = np.sum(fun(x)) ^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3093: in fun return np.power(x, n) * self._pmf(x, *args) ^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:56: in _pmf return np.exp(self._logpmf(x, mu, w)) ^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:48: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43.]), 12, 0) f1 = . at 0x3f7fd51dd0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ____________________ TestZIGeneralizedPoisson.test_pmf_zero ____________________ cond = array([ True]) args = (array([3]), array([2]), array([1]), array([1]), array([0])) f1 = . at 0x3f7fd51c70> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_pmf_zero(self): gp_pmf = genpoisson_p.pmf(3, 2, 1, 1) > zigp_pmf = zigenpoisson.pmf(3, 2, 1, 1, 0) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/tests/test_discrete.py:135: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:106: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:95: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True]) args = (array([3]), array([2]), array([1]), array([1]), array([0])) f1 = . at 0x3f7fd51c70> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError __________________ TestZIGeneralizedPoisson.test_logpmf_zero ___________________ cond = array([ True]) args = (array([7]), array([3]), array([1]), array([1]), array([0])) f1 = . at 0x3f7f58c510> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_logpmf_zero(self): gp_logpmf = genpoisson_p.logpmf(7, 3, 1, 1) > zigp_logpmf = zigenpoisson.logpmf(7, 3, 1, 1, 0) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/tests/test_discrete.py:140: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3578: in logpmf place(output, cond, self._logpmf(*goodargs)) ^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:95: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True]) args = (array([7]), array([3]), array([1]), array([1]), array([0])) f1 = . at 0x3f7f58c510> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ______________________ TestZIGeneralizedPoisson.test_pmf _______________________ cond = array([ True]) args = (array([3]), array([2]), array([2]), array([2]), array([0.1])) f1 = . at 0x3f7f58c3b0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_pmf(self): gp_pmf = genpoisson_p.pmf(3, 2, 2, 2) > zigp_pmf = zigenpoisson.pmf(3, 2, 2, 2, 0.1) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/tests/test_discrete.py:145: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:106: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:95: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True]) args = (array([3]), array([2]), array([2]), array([2]), array([0.1])) f1 = . at 0x3f7f58c3b0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError _____________________ TestZIGeneralizedPoisson.test_logpmf _____________________ cond = array([ True]) args = (array([2]), array([3]), array([0]), array([2]), array([0.1])) f1 = . at 0x3f7f58c5c0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_logpmf(self): gp_logpmf = genpoisson_p.logpmf(2, 3, 0, 2) > zigp_logpmf = zigenpoisson.logpmf(2, 3, 0, 2, 0.1) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/tests/test_discrete.py:150: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3578: in logpmf place(output, cond, self._logpmf(*goodargs)) ^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:95: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True]) args = (array([2]), array([3]), array([0]), array([2]), array([0.1])) f1 = . at 0x3f7f58c5c0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ____________________________ TestZiNBP.test_pmf_p2 _____________________________ cond = array([ True]) args = (array([100]), array([10.]), array([0.25]), array([0.01])) f1 = . at 0x3f7ff20040> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_pmf_p2(self): n, p = zinegbin.convert_params(30, 0.1, 2) nb_pmf = nbinom.pmf(100, n, p) > tnb_pmf = zinegbin.pmf(100, 30, 0.1, 2, 0.01) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/tests/test_discrete.py:169: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:139: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:131: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True]) args = (array([100]), array([10.]), array([0.25]), array([0.01])) f1 = . at 0x3f7ff20040> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ___________________________ TestZiNBP.test_logpmf_p2 ___________________________ cond = array([ True]) args = (array([200]), array([1.]), array([0.09090909]), array([0.01])) f1 = . at 0x3f7f58c1a0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_logpmf_p2(self): n, p = zinegbin.convert_params(10, 1, 2) nb_logpmf = nbinom.logpmf(200, n, p) > tnb_logpmf = zinegbin.logpmf(200, 10, 1, 2, 0.01) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/tests/test_discrete.py:175: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3578: in logpmf place(output, cond, self._logpmf(*goodargs)) ^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:131: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True]) args = (array([200]), array([1.]), array([0.09090909]), array([0.01])) f1 = . at 0x3f7f58c1a0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError __________________________ TestZiNBP.test_moments_p2 ___________________________ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([ 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36.]), 1.0, 0.125, 0) f1 = . at 0x3f7ff21590> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_moments_p2(self): n, p = zinegbin.convert_params(7, 1, 2) nb_m1, nb_m2 = nbinom.moment(1, n, p), nbinom.moment(2, n, p) zinb_m0 = zinegbin.moment(0, 7, 1, 2, 0) > zinb_m1 = zinegbin.moment(1, 7, 1, 2, 0) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/tests/test_discrete.py:202: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3973: in moment return super().moment(order, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:1339: in moment val[...] = _moment_from_stats(n, mu, mu2, g1, g2, self._munp, shapes) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:384: in _moment_from_stats val = moment_func(1, *args) ^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:929: in _munp vals = self.generic_moment(n, *args) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2529: in __call__ return self._call_as_normal(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2522: in _call_as_normal return self._vectorize_call(func=func, args=vargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2610: in _vectorize_call outputs = ufunc(*args, out=...) ^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3096: in _drv2_moment return _expect(fun, _a, _b, self._ppf(0.5, *args), self.inc) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:4019: in _expect delta = np.sum(fun(x)) ^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3093: in fun return np.power(x, n) * self._pmf(x, *args) ^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:139: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:131: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([ 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36.]), 1.0, 0.125, 0) f1 = . at 0x3f7ff21590> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ______________________________ TestZiNBP.test_pmf ______________________________ cond = array([ True]) args = (array([2]), array([1.11111111]), array([0.52631579]), array([0.5])) f1 = . at 0x3f7ff21170> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_pmf(self): n, p = zinegbin.convert_params(1, 0.9, 1) nb_logpmf = nbinom.pmf(2, n, p) > tnb_pmf = zinegbin.pmf(2, 1, 0.9, 2, 0.5) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/tests/test_discrete.py:211: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3539: in pmf place(output, cond, np.clip(self._pmf(*goodargs), 0, 1)) ^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:139: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:131: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True]) args = (array([2]), array([1.11111111]), array([0.52631579]), array([0.5])) f1 = . at 0x3f7ff21170> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ____________________________ TestZiNBP.test_logpmf _____________________________ cond = array([ True]) args = (array([2]), array([5.]), array([0.5]), array([0.005])) f1 = . at 0x3f7ff20f60> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_logpmf(self): n, p = zinegbin.convert_params(5, 1, 1) nb_logpmf = nbinom.logpmf(2, n, p) > tnb_logpmf = zinegbin.logpmf(2, 5, 1, 1, 0.005) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/tests/test_discrete.py:217: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3578: in logpmf place(output, cond, self._logpmf(*goodargs)) ^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:131: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True]) args = (array([2]), array([5.]), array([0.5]), array([0.005])) f1 = . at 0x3f7ff20f60> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError ____________________________ TestZiNBP.test_moments ____________________________ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([ 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39.]), 9.0, 0.5, 0) f1 = . at 0x3f7ff9acf0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: > import scipy._lib.array_api_extra as xpx E ModuleNotFoundError: No module named 'scipy._lib.array_api_extra' statsmodels/compat/scipy.py:129: ModuleNotFoundError During handling of the above exception, another exception occurred: self = def test_moments(self): n, p = zinegbin.convert_params(9, 1, 1) nb_m1, nb_m2 = nbinom.moment(1, n, p), nbinom.moment(2, n, p) zinb_m0 = zinegbin.moment(0, 9, 1, 1, 0) > zinb_m1 = zinegbin.moment(1, 9, 1, 1, 0) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/tests/test_discrete.py:256: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3973: in moment return super().moment(order, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:1339: in moment val[...] = _moment_from_stats(n, mu, mu2, g1, g2, self._munp, shapes) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:384: in _moment_from_stats val = moment_func(1, *args) ^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:929: in _munp vals = self.generic_moment(n, *args) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2529: in __call__ return self._call_as_normal(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2522: in _call_as_normal return self._vectorize_call(func=func, args=vargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/numpy/lib/_function_base_impl.py:2610: in _vectorize_call outputs = ufunc(*args, out=...) ^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3096: in _drv2_moment return _expect(fun, _a, _b, self._ppf(0.5, *args), self.inc) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:4019: in _expect delta = np.sum(fun(x)) ^^^^^^ /usr/lib/python3.14/site-packages/scipy/stats/_distn_infrastructure.py:3093: in fun return np.power(x, n) * self._pmf(x, *args) ^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:139: in _pmf return np.exp(self._logpmf(x, mu, alpha, p, w)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/distributions/discrete.py:131: in _logpmf return apply_where( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ cond = array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True,... True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) args = (array([ 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39.]), 9.0, 0.5, 0) f1 = . at 0x3f7ff9acf0> f2 = None def apply_where( # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02 cond, args, f1, f2=None, /, *, fill_value=None ):  """  Run one of two elementwise functions depending on a condition.   Equivalent to ``f1(*args) if cond else fill_value`` performed elementwise  when `fill_value` is defined, otherwise to ``f1(*args) if cond else f2(*args)``.   Parameters  ----------  cond : array  The condition, expressed as a boolean array.  args : Array or tuple of Arrays  Argument(s) to `f1` (and `f2`). Must be broadcastable with `cond`.  f1 : callable  Elementwise function of `args`, returning a single array.  Where `cond` is True, output will be ``f1(arg0[cond], arg1[cond], ...)``.  f2 : callable, optional  Elementwise function of `args`, returning a single array.  Where `cond` is False, output will be ``f2(arg0[cond], arg1[cond], ...)``.  Mutually exclusive with `fill_value`.  fill_value : Array or scalar, optional  If provided, value with which to fill output array where `cond` is False.  It does not need to be scalar; it needs however to be broadcastable with  `cond` and `args`.  Mutually exclusive with `f2`. You must provide one or the other.  xp : array_namespace, optional  The standard-compatible namespace for `cond` and `args`. Default: infer.   Returns  -------  Array  An array with elements from the output of `f1` where `cond` is True and either  the output of `f2` or `fill_value` where `cond` is False. The returned array has  data type determined by type promotion rules between the output of `f1` and  either `fill_value` or the output of `f2`.   Notes  -----  Falls back to _lazywhere if xpx.apply_where is not available.   ``xp.where(cond, f1(*args), f2(*args))`` requires explicitly evaluating `f1` even  when `cond` is False, and `f2` when cond is True. This function evaluates each  function only for their matching condition, if the backend allows for it.   On Dask, `f1` and `f2` are applied to the individual chunks and should use functions  from the namespace of the chunks.   """ try: import scipy._lib.array_api_extra as xpx  return xpx.apply_where(cond, args, f1, f2, fill_value=fill_value) except (ImportError, AttributeError): > from scipy._lib._util import _lazywhere E ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) statsmodels/compat/scipy.py:133: ImportError __________________________________ test_equi ___________________________________ def test_equi(): # Test the structure of the equivariant knockoff construction.  np.random.seed(2342) exog = np.random.normal(size=(10, 4))  > exog1, exog2, sl = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:23: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[-0.32044952, -0.27117563, -0.33286912, 0.54409599], [ 0.20457187, -0.07191398, 0.20568316, 0.1108813... [-0.00640112, -0.06445187, 0.10770326, -0.00507743], [-0.40387752, -0.40209055, 0.29420265, -0.57742865]]) xcov = array([[ 1. , -0.28127773, 0.2107379 , -0.07243798], [-0.28127773, 1. , -0.25782581, 0.2271288... [ 0.2107379 , -0.25782581, 1. , -0.53591334], [-0.07243798, 0.2271288 , -0.53591334, 1. ]]) sl = array([0.88916714+0.j, 0.88916714+0.j, 0.88916714+0.j, 0.88916714+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError ________________________ test_testers[equi-tester0-49] _________________________ p = 49 tester = method = 'equi' @pytest.mark.parametrize("p", [49, 50]) @pytest.mark.parametrize("tester", [ kr.CorrelationEffects(), kr.ForwardEffects(pursuit=False), kr.ForwardEffects(pursuit=True), kr.OLSEffects(), kr.RegModelEffects(sm.OLS), kr.RegModelEffects(sm.OLS, True, fit_kws={"L1_wt": 0, "alpha": 1}), ]) @pytest.mark.parametrize("method", ["equi", "sdp"]) def test_testers(p, tester, method):  if method == "sdp" and not has_cvxopt: return  np.random.seed(2432) n = 200  y = np.random.normal(size=n) x = np.random.normal(size=(n, p))  > kn = RegressionFDR(y, x, tester, design_method=method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:82: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:90: in __init__ exog1, exog2, _ = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[-0.11072201, -0.0503404 , 0.07599664, ..., -0.02372056, 0.07932165, -0.02168184], [-0.0030457..., [-0.0118116 , -0.03766278, 0.08838746, ..., 0.0566458 , -0.08638474, -0.11412388]], shape=(200, 49)) xcov = array([[ 1. , 0.04307993, 0.04188109, ..., -0.08663161, -0.11979277, 0.01227774], [ 0.0430799...], [ 0.01227774, 0.00432148, 0.01318136, ..., -0.03011487, 0.03515259, 1. ]], shape=(49, 49)) sl = array([0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.598554...418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError ________________________ test_testers[equi-tester0-50] _________________________ p = 50 tester = method = 'equi' @pytest.mark.parametrize("p", [49, 50]) @pytest.mark.parametrize("tester", [ kr.CorrelationEffects(), kr.ForwardEffects(pursuit=False), kr.ForwardEffects(pursuit=True), kr.OLSEffects(), kr.RegModelEffects(sm.OLS), kr.RegModelEffects(sm.OLS, True, fit_kws={"L1_wt": 0, "alpha": 1}), ]) @pytest.mark.parametrize("method", ["equi", "sdp"]) def test_testers(p, tester, method):  if method == "sdp" and not has_cvxopt: return  np.random.seed(2432) n = 200  y = np.random.normal(size=n) x = np.random.normal(size=(n, p))  > kn = RegressionFDR(y, x, tester, design_method=method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:82: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:90: in __init__ exog1, exog2, _ = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[-0.12149614, -0.04899979, 0.07372147, ..., 0.07972735, -0.02286368, -0.00340466], [-0.0498288..., [-0.00289337, -0.03772741, -0.10556863, ..., 0.05838274, -0.02010134, -0.08237645]], shape=(200, 50)) xcov = array([[ 1. , 0.05253097, -0.06159911, ..., -0.03580109, -0.05332988, 0.00269265], [ 0.0525309...], [ 0.00269265, 0.01389336, 0.01023944, ..., 0.07962455, -0.02144613, 1. ]], shape=(50, 50)) sl = array([0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j,...0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError ________________________ test_testers[equi-tester1-49] _________________________ p = 49 tester = method = 'equi' @pytest.mark.parametrize("p", [49, 50]) @pytest.mark.parametrize("tester", [ kr.CorrelationEffects(), kr.ForwardEffects(pursuit=False), kr.ForwardEffects(pursuit=True), kr.OLSEffects(), kr.RegModelEffects(sm.OLS), kr.RegModelEffects(sm.OLS, True, fit_kws={"L1_wt": 0, "alpha": 1}), ]) @pytest.mark.parametrize("method", ["equi", "sdp"]) def test_testers(p, tester, method):  if method == "sdp" and not has_cvxopt: return  np.random.seed(2432) n = 200  y = np.random.normal(size=n) x = np.random.normal(size=(n, p))  > kn = RegressionFDR(y, x, tester, design_method=method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:82: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:90: in __init__ exog1, exog2, _ = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[-0.11072201, -0.0503404 , 0.07599664, ..., -0.02372056, 0.07932165, -0.02168184], [-0.0030457..., [-0.0118116 , -0.03766278, 0.08838746, ..., 0.0566458 , -0.08638474, -0.11412388]], shape=(200, 49)) xcov = array([[ 1. , 0.04307993, 0.04188109, ..., -0.08663161, -0.11979277, 0.01227774], [ 0.0430799...], [ 0.01227774, 0.00432148, 0.01318136, ..., -0.03011487, 0.03515259, 1. ]], shape=(49, 49)) sl = array([0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.598554...418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError ________________________ test_testers[equi-tester1-50] _________________________ p = 50 tester = method = 'equi' @pytest.mark.parametrize("p", [49, 50]) @pytest.mark.parametrize("tester", [ kr.CorrelationEffects(), kr.ForwardEffects(pursuit=False), kr.ForwardEffects(pursuit=True), kr.OLSEffects(), kr.RegModelEffects(sm.OLS), kr.RegModelEffects(sm.OLS, True, fit_kws={"L1_wt": 0, "alpha": 1}), ]) @pytest.mark.parametrize("method", ["equi", "sdp"]) def test_testers(p, tester, method):  if method == "sdp" and not has_cvxopt: return  np.random.seed(2432) n = 200  y = np.random.normal(size=n) x = np.random.normal(size=(n, p))  > kn = RegressionFDR(y, x, tester, design_method=method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:82: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:90: in __init__ exog1, exog2, _ = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[-0.12149614, -0.04899979, 0.07372147, ..., 0.07972735, -0.02286368, -0.00340466], [-0.0498288..., [-0.00289337, -0.03772741, -0.10556863, ..., 0.05838274, -0.02010134, -0.08237645]], shape=(200, 50)) xcov = array([[ 1. , 0.05253097, -0.06159911, ..., -0.03580109, -0.05332988, 0.00269265], [ 0.0525309...], [ 0.00269265, 0.01389336, 0.01023944, ..., 0.07962455, -0.02144613, 1. ]], shape=(50, 50)) sl = array([0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j,...0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError ________________________ test_testers[equi-tester2-49] _________________________ p = 49 tester = method = 'equi' @pytest.mark.parametrize("p", [49, 50]) @pytest.mark.parametrize("tester", [ kr.CorrelationEffects(), kr.ForwardEffects(pursuit=False), kr.ForwardEffects(pursuit=True), kr.OLSEffects(), kr.RegModelEffects(sm.OLS), kr.RegModelEffects(sm.OLS, True, fit_kws={"L1_wt": 0, "alpha": 1}), ]) @pytest.mark.parametrize("method", ["equi", "sdp"]) def test_testers(p, tester, method):  if method == "sdp" and not has_cvxopt: return  np.random.seed(2432) n = 200  y = np.random.normal(size=n) x = np.random.normal(size=(n, p))  > kn = RegressionFDR(y, x, tester, design_method=method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:82: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:90: in __init__ exog1, exog2, _ = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[-0.11072201, -0.0503404 , 0.07599664, ..., -0.02372056, 0.07932165, -0.02168184], [-0.0030457..., [-0.0118116 , -0.03766278, 0.08838746, ..., 0.0566458 , -0.08638474, -0.11412388]], shape=(200, 49)) xcov = array([[ 1. , 0.04307993, 0.04188109, ..., -0.08663161, -0.11979277, 0.01227774], [ 0.0430799...], [ 0.01227774, 0.00432148, 0.01318136, ..., -0.03011487, 0.03515259, 1. ]], shape=(49, 49)) sl = array([0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.598554...418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError ________________________ test_testers[equi-tester2-50] _________________________ p = 50 tester = method = 'equi' @pytest.mark.parametrize("p", [49, 50]) @pytest.mark.parametrize("tester", [ kr.CorrelationEffects(), kr.ForwardEffects(pursuit=False), kr.ForwardEffects(pursuit=True), kr.OLSEffects(), kr.RegModelEffects(sm.OLS), kr.RegModelEffects(sm.OLS, True, fit_kws={"L1_wt": 0, "alpha": 1}), ]) @pytest.mark.parametrize("method", ["equi", "sdp"]) def test_testers(p, tester, method):  if method == "sdp" and not has_cvxopt: return  np.random.seed(2432) n = 200  y = np.random.normal(size=n) x = np.random.normal(size=(n, p))  > kn = RegressionFDR(y, x, tester, design_method=method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:82: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:90: in __init__ exog1, exog2, _ = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[-0.12149614, -0.04899979, 0.07372147, ..., 0.07972735, -0.02286368, -0.00340466], [-0.0498288..., [-0.00289337, -0.03772741, -0.10556863, ..., 0.05838274, -0.02010134, -0.08237645]], shape=(200, 50)) xcov = array([[ 1. , 0.05253097, -0.06159911, ..., -0.03580109, -0.05332988, 0.00269265], [ 0.0525309...], [ 0.00269265, 0.01389336, 0.01023944, ..., 0.07962455, -0.02144613, 1. ]], shape=(50, 50)) sl = array([0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j,...0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError ________________________ test_testers[equi-tester3-49] _________________________ p = 49 tester = method = 'equi' @pytest.mark.parametrize("p", [49, 50]) @pytest.mark.parametrize("tester", [ kr.CorrelationEffects(), kr.ForwardEffects(pursuit=False), kr.ForwardEffects(pursuit=True), kr.OLSEffects(), kr.RegModelEffects(sm.OLS), kr.RegModelEffects(sm.OLS, True, fit_kws={"L1_wt": 0, "alpha": 1}), ]) @pytest.mark.parametrize("method", ["equi", "sdp"]) def test_testers(p, tester, method):  if method == "sdp" and not has_cvxopt: return  np.random.seed(2432) n = 200  y = np.random.normal(size=n) x = np.random.normal(size=(n, p))  > kn = RegressionFDR(y, x, tester, design_method=method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:82: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:90: in __init__ exog1, exog2, _ = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[-0.11072201, -0.0503404 , 0.07599664, ..., -0.02372056, 0.07932165, -0.02168184], [-0.0030457..., [-0.0118116 , -0.03766278, 0.08838746, ..., 0.0566458 , -0.08638474, -0.11412388]], shape=(200, 49)) xcov = array([[ 1. , 0.04307993, 0.04188109, ..., -0.08663161, -0.11979277, 0.01227774], [ 0.0430799...], [ 0.01227774, 0.00432148, 0.01318136, ..., -0.03011487, 0.03515259, 1. ]], shape=(49, 49)) sl = array([0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.598554...418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError ________________________ test_testers[equi-tester3-50] _________________________ p = 50 tester = method = 'equi' @pytest.mark.parametrize("p", [49, 50]) @pytest.mark.parametrize("tester", [ kr.CorrelationEffects(), kr.ForwardEffects(pursuit=False), kr.ForwardEffects(pursuit=True), kr.OLSEffects(), kr.RegModelEffects(sm.OLS), kr.RegModelEffects(sm.OLS, True, fit_kws={"L1_wt": 0, "alpha": 1}), ]) @pytest.mark.parametrize("method", ["equi", "sdp"]) def test_testers(p, tester, method):  if method == "sdp" and not has_cvxopt: return  np.random.seed(2432) n = 200  y = np.random.normal(size=n) x = np.random.normal(size=(n, p))  > kn = RegressionFDR(y, x, tester, design_method=method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:82: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:90: in __init__ exog1, exog2, _ = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[-0.12149614, -0.04899979, 0.07372147, ..., 0.07972735, -0.02286368, -0.00340466], [-0.0498288..., [-0.00289337, -0.03772741, -0.10556863, ..., 0.05838274, -0.02010134, -0.08237645]], shape=(200, 50)) xcov = array([[ 1. , 0.05253097, -0.06159911, ..., -0.03580109, -0.05332988, 0.00269265], [ 0.0525309...], [ 0.00269265, 0.01389336, 0.01023944, ..., 0.07962455, -0.02144613, 1. ]], shape=(50, 50)) sl = array([0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j,...0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError ________________________ test_testers[equi-tester4-49] _________________________ p = 49 tester = method = 'equi' @pytest.mark.parametrize("p", [49, 50]) @pytest.mark.parametrize("tester", [ kr.CorrelationEffects(), kr.ForwardEffects(pursuit=False), kr.ForwardEffects(pursuit=True), kr.OLSEffects(), kr.RegModelEffects(sm.OLS), kr.RegModelEffects(sm.OLS, True, fit_kws={"L1_wt": 0, "alpha": 1}), ]) @pytest.mark.parametrize("method", ["equi", "sdp"]) def test_testers(p, tester, method):  if method == "sdp" and not has_cvxopt: return  np.random.seed(2432) n = 200  y = np.random.normal(size=n) x = np.random.normal(size=(n, p))  > kn = RegressionFDR(y, x, tester, design_method=method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:82: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:90: in __init__ exog1, exog2, _ = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[-0.11072201, -0.0503404 , 0.07599664, ..., -0.02372056, 0.07932165, -0.02168184], [-0.0030457..., [-0.0118116 , -0.03766278, 0.08838746, ..., 0.0566458 , -0.08638474, -0.11412388]], shape=(200, 49)) xcov = array([[ 1. , 0.04307993, 0.04188109, ..., -0.08663161, -0.11979277, 0.01227774], [ 0.0430799...], [ 0.01227774, 0.00432148, 0.01318136, ..., -0.03011487, 0.03515259, 1. ]], shape=(49, 49)) sl = array([0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.598554...418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError ________________________ test_testers[equi-tester4-50] _________________________ p = 50 tester = method = 'equi' @pytest.mark.parametrize("p", [49, 50]) @pytest.mark.parametrize("tester", [ kr.CorrelationEffects(), kr.ForwardEffects(pursuit=False), kr.ForwardEffects(pursuit=True), kr.OLSEffects(), kr.RegModelEffects(sm.OLS), kr.RegModelEffects(sm.OLS, True, fit_kws={"L1_wt": 0, "alpha": 1}), ]) @pytest.mark.parametrize("method", ["equi", "sdp"]) def test_testers(p, tester, method):  if method == "sdp" and not has_cvxopt: return  np.random.seed(2432) n = 200  y = np.random.normal(size=n) x = np.random.normal(size=(n, p))  > kn = RegressionFDR(y, x, tester, design_method=method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:82: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:90: in __init__ exog1, exog2, _ = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[-0.12149614, -0.04899979, 0.07372147, ..., 0.07972735, -0.02286368, -0.00340466], [-0.0498288..., [-0.00289337, -0.03772741, -0.10556863, ..., 0.05838274, -0.02010134, -0.08237645]], shape=(200, 50)) xcov = array([[ 1. , 0.05253097, -0.06159911, ..., -0.03580109, -0.05332988, 0.00269265], [ 0.0525309...], [ 0.00269265, 0.01389336, 0.01023944, ..., 0.07962455, -0.02144613, 1. ]], shape=(50, 50)) sl = array([0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j,...0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError ________________________ test_testers[equi-tester5-49] _________________________ p = 49 tester = method = 'equi' @pytest.mark.parametrize("p", [49, 50]) @pytest.mark.parametrize("tester", [ kr.CorrelationEffects(), kr.ForwardEffects(pursuit=False), kr.ForwardEffects(pursuit=True), kr.OLSEffects(), kr.RegModelEffects(sm.OLS), kr.RegModelEffects(sm.OLS, True, fit_kws={"L1_wt": 0, "alpha": 1}), ]) @pytest.mark.parametrize("method", ["equi", "sdp"]) def test_testers(p, tester, method):  if method == "sdp" and not has_cvxopt: return  np.random.seed(2432) n = 200  y = np.random.normal(size=n) x = np.random.normal(size=(n, p))  > kn = RegressionFDR(y, x, tester, design_method=method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:82: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:90: in __init__ exog1, exog2, _ = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[-0.11072201, -0.0503404 , 0.07599664, ..., -0.02372056, 0.07932165, -0.02168184], [-0.0030457..., [-0.0118116 , -0.03766278, 0.08838746, ..., 0.0566458 , -0.08638474, -0.11412388]], shape=(200, 49)) xcov = array([[ 1. , 0.04307993, 0.04188109, ..., -0.08663161, -0.11979277, 0.01227774], [ 0.0430799...], [ 0.01227774, 0.00432148, 0.01318136, ..., -0.03011487, 0.03515259, 1. ]], shape=(49, 49)) sl = array([0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.598554...418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j, 0.59855418+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError ________________________ test_testers[equi-tester5-50] _________________________ p = 50 tester = method = 'equi' @pytest.mark.parametrize("p", [49, 50]) @pytest.mark.parametrize("tester", [ kr.CorrelationEffects(), kr.ForwardEffects(pursuit=False), kr.ForwardEffects(pursuit=True), kr.OLSEffects(), kr.RegModelEffects(sm.OLS), kr.RegModelEffects(sm.OLS, True, fit_kws={"L1_wt": 0, "alpha": 1}), ]) @pytest.mark.parametrize("method", ["equi", "sdp"]) def test_testers(p, tester, method):  if method == "sdp" and not has_cvxopt: return  np.random.seed(2432) n = 200  y = np.random.normal(size=n) x = np.random.normal(size=(n, p))  > kn = RegressionFDR(y, x, tester, design_method=method) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:82: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:90: in __init__ exog1, exog2, _ = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[-0.12149614, -0.04899979, 0.07372147, ..., 0.07972735, -0.02286368, -0.00340466], [-0.0498288..., [-0.00289337, -0.03772741, -0.10556863, ..., 0.05838274, -0.02010134, -0.08237645]], shape=(200, 50)) xcov = array([[ 1. , 0.05253097, -0.06159911, ..., -0.03580109, -0.05332988, 0.00269265], [ 0.0525309...], [ 0.00269265, 0.01389336, 0.01023944, ..., 0.07962455, -0.02144613, 1. ]], shape=(50, 50)) sl = array([0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j,...0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j, 0.5855343+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError _______________________ test_sim[tester0-300-100-6-equi] _______________________ method = 'equi' tester = n = 300, p = 100, es = 6 @pytest.mark.slow @pytest.mark.parametrize("method", ["equi", "sdp"]) @pytest.mark.parametrize("tester,n,p,es", [ [kr.CorrelationEffects(), 300, 100, 6], [kr.ForwardEffects(pursuit=False), 300, 100, 3.5], [kr.ForwardEffects(pursuit=True), 300, 100, 3.5], [kr.OLSEffects(), 3000, 200, 3.5], ]) def test_sim(method, tester, n, p, es): # This function assesses the performance of the knockoff approach # relative to its theoretical claims.  if method == "sdp" and not has_cvxopt: return  np.random.seed(43234)  # Number of variables with a non-zero coefficient npos = 30  # Aim to control FDR to this level target_fdr = 0.2  # Number of siumulation replications nrep = 10  if method == "sdp" and not has_cvxopt: return  fdr, power = 0, 0 for k in range(nrep):  # Generate the predictors x = np.random.normal(size=(n, p)) x /= np.sqrt(np.sum(x*x, 0))  # Generate the response variable coeff = es * (-1)**np.arange(npos) y = np.dot(x[:, 0:npos], coeff) + np.random.normal(size=n)  > kn = RegressionFDR(y, x, tester) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:128: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:90: in __init__ exog1, exog2, _ = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[ 0.03730471, -0.0659656 , 0.03273128, ..., 0.07025048, 0.05779821, -0.06333034], [ 0.0036247... [-0.01072637, 0.04073203, -0.02927214, ..., -0.00857912, 0.03333247, -0.03033854]], shape=(300, 100)) xcov = array([[ 1. , 0.04594072, 0.06136215, ..., -0.05249386, -0.02319295, 0.07094251], [ 0.0459407... [ 0.07094251, -0.03139209, 0.125898 , ..., -0.0082934 , -0.05668502, 1. ]], shape=(100, 100)) sl = array([0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j,...7+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError ______________________ test_sim[tester1-300-100-3.5-equi] ______________________ method = 'equi' tester = n = 300, p = 100, es = 3.5 @pytest.mark.slow @pytest.mark.parametrize("method", ["equi", "sdp"]) @pytest.mark.parametrize("tester,n,p,es", [ [kr.CorrelationEffects(), 300, 100, 6], [kr.ForwardEffects(pursuit=False), 300, 100, 3.5], [kr.ForwardEffects(pursuit=True), 300, 100, 3.5], [kr.OLSEffects(), 3000, 200, 3.5], ]) def test_sim(method, tester, n, p, es): # This function assesses the performance of the knockoff approach # relative to its theoretical claims.  if method == "sdp" and not has_cvxopt: return  np.random.seed(43234)  # Number of variables with a non-zero coefficient npos = 30  # Aim to control FDR to this level target_fdr = 0.2  # Number of siumulation replications nrep = 10  if method == "sdp" and not has_cvxopt: return  fdr, power = 0, 0 for k in range(nrep):  # Generate the predictors x = np.random.normal(size=(n, p)) x /= np.sqrt(np.sum(x*x, 0))  # Generate the response variable coeff = es * (-1)**np.arange(npos) y = np.dot(x[:, 0:npos], coeff) + np.random.normal(size=n)  > kn = RegressionFDR(y, x, tester) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:128: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:90: in __init__ exog1, exog2, _ = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[ 0.03730471, -0.0659656 , 0.03273128, ..., 0.07025048, 0.05779821, -0.06333034], [ 0.0036247... [-0.01072637, 0.04073203, -0.02927214, ..., -0.00857912, 0.03333247, -0.03033854]], shape=(300, 100)) xcov = array([[ 1. , 0.04594072, 0.06136215, ..., -0.05249386, -0.02319295, 0.07094251], [ 0.0459407... [ 0.07094251, -0.03139209, 0.125898 , ..., -0.0082934 , -0.05668502, 1. ]], shape=(100, 100)) sl = array([0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j,...7+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError ______________________ test_sim[tester2-300-100-3.5-equi] ______________________ method = 'equi' tester = n = 300, p = 100, es = 3.5 @pytest.mark.slow @pytest.mark.parametrize("method", ["equi", "sdp"]) @pytest.mark.parametrize("tester,n,p,es", [ [kr.CorrelationEffects(), 300, 100, 6], [kr.ForwardEffects(pursuit=False), 300, 100, 3.5], [kr.ForwardEffects(pursuit=True), 300, 100, 3.5], [kr.OLSEffects(), 3000, 200, 3.5], ]) def test_sim(method, tester, n, p, es): # This function assesses the performance of the knockoff approach # relative to its theoretical claims.  if method == "sdp" and not has_cvxopt: return  np.random.seed(43234)  # Number of variables with a non-zero coefficient npos = 30  # Aim to control FDR to this level target_fdr = 0.2  # Number of siumulation replications nrep = 10  if method == "sdp" and not has_cvxopt: return  fdr, power = 0, 0 for k in range(nrep):  # Generate the predictors x = np.random.normal(size=(n, p)) x /= np.sqrt(np.sum(x*x, 0))  # Generate the response variable coeff = es * (-1)**np.arange(npos) y = np.dot(x[:, 0:npos], coeff) + np.random.normal(size=n)  > kn = RegressionFDR(y, x, tester) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/tests/test_knockoff.py:128: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ statsmodels/stats/_knockoff.py:90: in __init__ exog1, exog2, _ = _design_knockoff_equi(exog) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ statsmodels/stats/_knockoff.py:229: in _design_knockoff_equi exogn = _get_knmat(exog, xcov, sl) ^^^^^^^^^^^^^^^^^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ exog = array([[ 0.03730471, -0.0659656 , 0.03273128, ..., 0.07025048, 0.05779821, -0.06333034], [ 0.0036247... [-0.01072637, 0.04073203, -0.02927214, ..., -0.00857912, 0.03333247, -0.03033854]], shape=(300, 100)) xcov = array([[ 1. , 0.04594072, 0.06136215, ..., -0.05249386, -0.02319295, 0.07094251], [ 0.0459407... [ 0.07094251, -0.03139209, 0.125898 , ..., -0.0082934 , -0.05668502, 1. ]], shape=(100, 100)) sl = array([0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j,...7+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j, 0.3909237+0.j]) def _get_knmat(exog, xcov, sl): # Utility function, see equation 2.2 of Barber & Candes.  nobs, nvar = exog.shape  ash = np.linalg.inv(xcov) > ash *= -np.outer(sl, sl) E numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' statsmodels/stats/_knockoff.py:240: UFuncTypeError =============================== warnings summary =============================== statsmodels/tools/validation/tests/test_validation.py:358 /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tools/validation/tests/test_validation.py:358: DeprecationWarning: The 'generic' unit for NumPy timedelta is deprecated, and will raise an error in the future. This includes implicit conversion of bare integers (e.g. `+ 1`).Please use a specific unit instead. np.timedelta64(2), ../../../../../../usr/lib/python3.14/site-packages/pandas/core/resample.py:2359: 1 warning tsa/statespace/tests/test_decompose.py: 2 warnings tsa/statespace/tests/test_dynamic_factor_mq.py: 165 warnings tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py: 70 warnings tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py: 5 warnings tsa/stl/tests/test_stl.py: 1 warning /usr/lib/python3.14/site-packages/pandas/core/resample.py:2359: DeprecationWarning: The 'generic' unit for NumPy timedelta is deprecated, and will raise an error in the future. This includes implicit conversion of bare integers (e.g. `+ 1`).Please use a specific unit instead. + Timedelta(days=1, unit=edges_dti.unit).as_unit(edges_dti.unit) base/tests/test_penalized.py: 2 warnings base/tests/test_shrink_pickle.py: 3 warnings discrete/tests/test_count_model.py: 2 warnings discrete/tests/test_discrete.py: 9 warnings tsa/forecasting/tests/test_stl.py: 1 warning tsa/statespace/tests/test_sarimax.py: 1 warning tsa/tests/test_exponential_smoothing.py: 1 warning tsa/vector_ar/tests/test_svar.py: 101 warnings /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals warnings.warn("Maximum Likelihood optimization failed to " discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_fit_regularized discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_fit_regularized discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_fit_regularized discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_fit_regularized /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 1 out of 4 parameters Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers warnings.warn(message, ConvergenceWarning) discrete/tests/test_count_model.py: 8 warnings discrete/tests/test_discrete.py: 2 warnings discrete/tests/test_truncated_model.py: 1 warning /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_solvers_common.py:144: ConvergenceWarning: Could not trim params automatically due to failed QC check. Trimming using trim_mode == 'size' will still work. warnings.warn(msg, ConvergenceWarning) discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_fit_regularized discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_fit_regularized discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_fit_regularized /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 2 out of 6 parameters Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers warnings.warn(message, ConvergenceWarning) discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_fit_regularized /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 1 out of 6 parameters Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers warnings.warn(message, ConvergenceWarning) discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_fit_regularized /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 3 out of 4 parameters Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers warnings.warn(message, ConvergenceWarning) discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_fit_regularized /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 2 out of 5 parameters Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers warnings.warn(message, ConvergenceWarning) discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_fit_regularized /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 3 out of 7 parameters Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers warnings.warn(message, ConvergenceWarning) discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_fit_regularized /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 1 out of 2 parameters Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers warnings.warn(message, ConvergenceWarning) discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_fit_regularized /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 1 out of 3 parameters Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers warnings.warn(message, ConvergenceWarning) discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_fit_regularized /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 1 out of 5 parameters Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers warnings.warn(message, ConvergenceWarning) discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP_predict2::test_mean /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/model.py:595: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available warnings.warn('Inverting hessian failed, no bse or cov_params ' discrete/tests/test_discrete.py::TestPoissonL1Compatability::test_params discrete/tests/test_discrete.py::TestNegativeBinomialGeoL1Compatability::test_params discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized /usr/lib/python3.14/site-packages/numpy/_core/fromnumeric.py:83: RuntimeWarning: overflow encountered in reduce return ufunc.reduce(obj, axis, dtype, out, **passkwargs) discrete/tests/test_discrete.py::test_perfect_prediction discrete/tests/test_discrete.py::test_perfect_prediction discrete/tests/test_discrete.py::test_perfect_prediction /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/discrete/discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified warnings.warn(msg, category=PerfectSeparationWarning) discrete/tests/test_discrete.py::test_negative_binomial_default_alpha_param /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families/links.py:13: FutureWarning: The nbinom link alias is deprecated. Use NegativeBinomial instead. The nbinom link alias will be removed after the 0.15.0 release. warnings.warn( discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 4 out of 10 parameters Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers warnings.warn(message, ConvergenceWarning) discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 5 out of 11 parameters Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers warnings.warn(message, ConvergenceWarning) discrete/tests/test_predict.py::TestNegativeBinomialPPredict::test_influence discrete/tests/test_predict.py::TestNegativeBinomialPPredict::test_predict discrete/tests/test_predict.py::TestGeneralizedPoissonPredict::test_influence discrete/tests/test_truncated_model.py::TestHurdlePoissonR::test_predict /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/_prediction_inference.py:782: UserWarning: using default log-link in get_prediction warnings.warn("using default log-link in get_prediction") discrete/tests/test_predict.py::test_distr[case9] discrete/tests/test_predict.py::test_distr[case10] discrete/tests/test_predict.py::test_distr[case11] /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/stats/outliers_influence.py:545: RuntimeWarning: invalid value encountered in sqrt return sf / np.sqrt(hf) / np.sqrt(1 - self.hat_matrix_diag) discrete/tests/test_truncated_model.py::TestZeroTruncatedNBPModel::test_fit_regularized /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 2 out of 4 parameters Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers warnings.warn(message, ConvergenceWarning) gam/tests/test_gam.py: 11 warnings gam/tests/test_penalized.py: 9 warnings gam/tests/test_smooth_basis.py: 1 warning /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/gam/smooth_basis.py:742: DeprecationWarning: Setting the shape on a NumPy array has been deprecated in NumPy 2.5. As an alternative, you can create a new view using np.reshape (with copy=False if needed). self.x.shape = (len(x), 1) genmod/tests/test_gee.py::TestGEE::test_invalid_args[False-True] genmod/tests/test_gee.py::TestGEE::test_invalid_args[True-True] /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/genmod/generalized_linear_model.py:314: RuntimeWarning: divide by zero encountered in log exposure = np.log(exposure) graphics/tests/test_tsaplots.py::test_predict_plot[None-True-model_and_args0] graphics/tests/test_tsaplots.py::test_predict_plot[None-True-model_and_args0] graphics/tests/test_tsaplots.py::test_predict_plot[None-True-model_and_args1] graphics/tests/test_tsaplots.py::test_predict_plot[None-True-model_and_args1] graphics/tests/test_tsaplots.py::test_predict_plot[0.1-True-model_and_args0] graphics/tests/test_tsaplots.py::test_predict_plot[0.1-True-model_and_args0] graphics/tests/test_tsaplots.py::test_predict_plot[0.1-True-model_and_args1] graphics/tests/test_tsaplots.py::test_predict_plot[0.1-True-model_and_args1] /usr/lib/python3.14/site-packages/pandas/plotting/_matplotlib/converter.py:1063: MatplotlibDeprecationWarning: The locs attribute was deprecated in Matplotlib 3.11 and will be removed in 3.13. self.locs: list[Any] = [] # unused, for matplotlib compat graphics/tests/test_tsaplots.py: 16 warnings /usr/lib/python3.14/site-packages/pandas/plotting/_matplotlib/converter.py:1087: MatplotlibDeprecationWarning: The locs attribute was deprecated in Matplotlib 3.11 and will be removed in 3.13. self.locs = locs graphics/tests/test_tsaplots.py: 4 warnings regression/tests/test_glsar_stata.py: 1 warning regression/tests/test_recursive_ls.py: 13 warnings stats/tests/test_diagnostic.py: 2 warnings tsa/arima/estimators/tests/test_gls.py: 1 warning tsa/arima/estimators/tests/test_innovations.py: 4 warnings tsa/arima/estimators/tests/test_statespace.py: 9 warnings tsa/arima/tests/test_model.py: 61 warnings tsa/forecasting/tests/test_stl.py: 5 warnings tsa/forecasting/tests/test_theta.py: 795 warnings tsa/innovations/tests/test_arma_innovations.py: 6 warnings tsa/innovations/tests/test_cython_arma_innovations_fast.py: 25 warnings tsa/statespace/tests/test_cfa_simulation_smoothing.py: 4 warnings tsa/statespace/tests/test_chandrasekhar.py: 11 warnings tsa/statespace/tests/test_concentrated.py: 12 warnings tsa/statespace/tests/test_conserve_memory.py: 42 warnings tsa/statespace/tests/test_dynamic_factor.py: 18 warnings tsa/statespace/tests/test_dynamic_factor_mq.py: 45 warnings tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py: 96 warnings tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py: 3 warnings tsa/statespace/tests/test_exact_diffuse_filtering.py: 5 warnings tsa/statespace/tests/test_exponential_smoothing.py: 56 warnings tsa/statespace/tests/test_fixed_params.py: 17 warnings tsa/statespace/tests/test_forecasting.py: 11 warnings tsa/statespace/tests/test_impulse_responses.py: 28 warnings tsa/statespace/tests/test_initialization.py: 12 warnings tsa/statespace/tests/test_kalman.py: 8 warnings tsa/statespace/tests/test_mlemodel.py: 45 warnings tsa/statespace/tests/test_models.py: 1 warning tsa/statespace/tests/test_multivariate_switch_univariate.py: 167 warnings tsa/statespace/tests/test_news.py: 183 warnings tsa/statespace/tests/test_pickle.py: 1 warning tsa/statespace/tests/test_prediction.py: 4 warnings tsa/statespace/tests/test_representation.py: 4 warnings tsa/statespace/tests/test_sarimax.py: 939 warnings tsa/statespace/tests/test_save.py: 7 warnings tsa/statespace/tests/test_simulate.py: 59 warnings tsa/statespace/tests/test_simulation_smoothing.py: 6 warnings tsa/statespace/tests/test_smoothing.py: 24 warnings tsa/statespace/tests/test_structural.py: 252 warnings tsa/statespace/tests/test_univariate.py: 2 warnings tsa/statespace/tests/test_varmax.py: 2 warnings tsa/statespace/tests/test_weights.py: 1 warning tsa/tests/test_ar.py: 2 warnings tsa/tests/test_arima_process.py: 2 warnings tsa/tests/test_exponential_smoothing.py: 2 warnings tsa/tests/test_stattools.py: 37 warnings /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/mlemodel.py:174: DeprecationWarning: Setting the shape on a NumPy array has been deprecated in NumPy 2.5. As an alternative, you can create a new view using np.reshape (with copy=False if needed). endog.shape = (endog.shape[0], 1) # this will be C-contiguous graphics/tests/test_tsaplots.py: 10 warnings /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/graphics/tests/test_tsaplots.py:388: DeprecationWarning: The 'generic' unit for NumPy timedelta is deprecated, and will raise an error in the future. This includes implicit conversion of bare integers (e.g. `+ 1`).Please use a specific unit instead. idx = [pd.Timestamp.now() + pd.Timedelta(seconds=i) for i in range(10)] nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_unordered_CV_LS /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/kernel_density.py:679: RuntimeWarning: invalid value encountered in scalar divide CV += (G / m_x ** 2) - 2 * (f_X_Y / m_x) nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_continuousdata_lc_cvls /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/kernel_regression.py:251: RuntimeWarning: invalid value encountered in divide B_x = (G_numer * d_fx - G_denom * d_mx) / (G_denom**2) nonparametric/tests/test_lowess.py::TestLowess::test_duplicate_xs /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/nonparametric/smoothers_lowess.py:226: RuntimeWarning: invalid value encountered in divide res, _ = _lowess(y, x, x, np.ones_like(x), regression/tests/test_dimred.py::test_covreduce /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/regression/dimred.py:694: ConvergenceWarning: CovReduce optimization did not converge, |g|=1.287955 warnings.warn(msg, ConvergenceWarning) regression/tests/test_glsar_gretl.py::TestGLSARGretl::test_all regression/tests/test_glsar_gretl.py::TestGLSARGretl::test_all /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/regression/linear_model.py:1288: DeprecationWarning: Setting the shape on a NumPy array has been deprecated in NumPy 2.5. As an alternative, you can create a new view using np.reshape (with copy=False if needed). self.rho.shape = (1,) regression/tests/test_processreg.py::test_formulas[True] /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/regression/process_regression.py:665: UserWarning: Fitting did not converge, |gradient|=0.000028 warnings.warn(msg) robust/tests/test_scale.py::TestHuberAxes::test_axis1 /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/robust/scale.py:292: RuntimeWarning: divide by zero encountered in divide subset = np.less_equal(np.abs((a - mu) / scale), self.c) sandbox/tests/test_gam.py::TestGAMGaussianLogLink::test_predict /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/sandbox/gam.py:327: IterationLimitWarning: Maximum iteration reached. warnings.warn(iteration_limit_doc, IterationLimitWarning) sandbox/tests/test_gam.py::TestGAMNegativeBinomial::test_predict /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/genmod/families/family.py:1367: ValueWarning: Negative binomial dispersion parameter alpha not set. Using default value alpha=1.0. warnings.warn("Negative binomial dispersion parameter alpha not " stats/tests/test_corrpsd.py::TestCovPSD::test_cov_nearest stats/tests/test_corrpsd.py::TestCorrPSD1::test_nearest stats/tests/test_corrpsd.py::test_corrpsd_threshold[0] stats/tests/test_corrpsd.py::test_corrpsd_threshold[1e-15] stats/tests/test_corrpsd.py::test_corrpsd_threshold[1e-10] stats/tests/test_corrpsd.py::test_corrpsd_threshold[1e-06] /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/stats/correlation_tools.py:89: IterationLimitWarning: Maximum iteration reached. warnings.warn(iteration_limit_doc, IterationLimitWarning) stats/tests/test_descriptivestats.py::test_description_basic stats/tests/test_descriptivestats.py::test_empty_columns stats/tests/test_descriptivestats.py::test_empty_columns /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/stats/descriptivestats.py:406: SmallSampleWarning: One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements. mode_res = stats.mode(ser_no_missing, **kwargs) stats/tests/test_power.py::test_power_solver_warn /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/stats/power.py:132: RuntimeWarning: invalid value encountered in sqrt pow_ = stats.norm.sf(crit - d*np.sqrt(nobs)/sigma) stats/tests/test_tost.py::test_tost_asym /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/stats/weightstats.py:1479: RuntimeWarning: invalid value encountered in log low = transform(low) tsa/base/tests/test_tsa_indexes.py::test_instantiation_valid tsa/base/tests/test_tsa_indexes.py::test_instantiation_valid tsa/base/tests/test_tsa_indexes.py::test_instantiation_valid tsa/base/tests/test_tsa_indexes.py::test_instantiation_valid /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/tsa_model.py:559: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. _index = to_datetime(index) tsa/holtwinters/tests/test_holtwinters.py::TestHoltWinters::test_holt_damp_r tsa/holtwinters/tests/test_holtwinters.py::test_no_params_to_optimize /usr/lib/python3.14/site-packages/pandas/util/_decorators.py:213: EstimationWarning: Model has no free parameters to estimate. Set optimized=False to suppress this warning return func(*args, **kwargs) tsa/holtwinters/tests/test_holtwinters.py::test_alternative_minimizers[trust-constr] /usr/lib/python3.14/site-packages/scipy/optimize/_differentiable_functions.py:385: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations. self.H.update(self.x - self.x_prev, self.g - self.g_prev) tsa/holtwinters/tests/test_holtwinters.py::test_forecast_index_types[irregular] tsa/holtwinters/tests/test_holtwinters.py::test_invalid_index /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/base/tsa_model.py:837: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception. return get_prediction_index( tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py::test_em_nonstationary tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py::test_em_nonstationary /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/genmod/generalized_linear_model.py:898: RuntimeWarning: divide by zero encountered in scalar divide return np.sum(resid / self.family.variance(mu)) / self.df_resid tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py::test_em_nonstationary /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/dynamic_factor_mq.py:2695: UserWarning: EM reached maximum number of iterations (2), without achieving convergence: llf=-23.123, convergence criterion=1.8362 (while specified tolerance was 1e-06) warn(f'EM reached maximum number of iterations ({maxiter}),' tsa/statespace/tests/test_exact_diffuse_filtering.py: 2 warnings tsa/statespace/tests/test_options.py: 1 warning tsa/statespace/tests/test_pickle.py: 1 warning tsa/statespace/tests/test_representation.py: 12 warnings /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/representation.py:777: DeprecationWarning: Setting the shape on a NumPy array has been deprecated in NumPy 2.5. As an alternative, you can create a new view using np.reshape (with copy=False if needed). endog.shape = (endog.shape[0], 1) tsa/statespace/tests/test_mlemodel.py::test_integer_params /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/kalman_filter.py:1762: RuntimeWarning: invalid value encountered in scalar divide self.scale = np.sum(scale_obs[d:]) / nobs_k_endog tsa/statespace/tests/test_sarimax.py::test_plot_too_few_obs /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/mlemodel.py:1235: RuntimeWarning: invalid value encountered in divide np.inner(score_obs, score_obs) / tsa/statespace/tests/test_sarimax.py::test_plot_too_few_obs /usr/lib/python3.14/site-packages/numpy/_core/fromnumeric.py:4270: RuntimeWarning: Degrees of freedom <= 0 for slice return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, tsa/statespace/tests/test_sarimax.py::test_plot_too_few_obs /usr/lib/python3.14/site-packages/numpy/_core/_methods.py:178: RuntimeWarning: invalid value encountered in divide arrmean = um.true_divide(arrmean, div, out=arrmean, tsa/statespace/tests/test_sarimax.py::test_plot_too_few_obs /usr/lib/python3.14/site-packages/numpy/_core/_methods.py:211: RuntimeWarning: invalid value encountered in scalar divide ret = ret.dtype.type(ret / rcount) tsa/statespace/tests/test_sarimax.py::test_sarimax_starting_values_few_obsevations_long_ma tsa/tests/test_stattools.py::test_arma_order_select_ic /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/statespace/sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters. warn('Non-invertible starting MA parameters found.' tsa/stl/tests/test_mstl.py::test_number_of_seasonal_components[data-periods2-None-2] tsa/stl/tests/test_mstl.py::test_output_invariant_to_period_order[data-periods_ordered1-windows_ordered1-periods_not_ordered1-windows_not_ordered1] /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/stl/mstl.py:218: UserWarning: A period(s) is larger than half the length of time series. Removing these period(s). warnings.warn( tsa/tests/test_exponential_smoothing.py::test_hessian /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/test_exponential_smoothing.py:669: PrecisionWarning: Calculation of the Hessian using finite differences is usually subject to substantial approximation errors. austourists_model_fit.model.hessian( tsa/tests/test_stattools.py::TestBreakvarHeteroskedasticityTest::test_2d_input_with_missing_values /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/test_stattools.py:461: UserWarning: Later subset of data for variable 2 has too few non-missing observations to calculate test statistic. actual_statistic, actual_pvalue = breakvar_heteroskedasticity_test( tsa/tests/test_stattools.py::TestKPSS::test_none /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/tests/test_stattools.py:890: InterpolationWarning: The test statistic is outside of the range of p-values available in the look-up table. The actual p-value is smaller than the p-value returned. kpss(self.x, nlags=None) tsa/tests/test_stattools.py::test_pacf_1_obs /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/regression/linear_model.py:1483: RuntimeWarning: invalid value encountered in scalar divide r[k] = (x[0:-k] * x[k:]).sum() / (n - k * adj_needed) tsa/tests/test_stattools.py::test_pacf_1_obs /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/regression/linear_model.py:1490: ValueWarning: Matrix is singular. Using pinv. warnings.warn("Matrix is singular. Using pinv.", ValueWarning) tsa/vector_ar/tests/test_coint.py::TestCointJoh12::test_basic tsa/vector_ar/tests/test_coint.py::TestCointJoh09::test_basic tsa/vector_ar/tests/test_coint.py::TestCointJohMin18::test_basic tsa/vector_ar/tests/test_coint.py::TestCointJoh25::test_basic tsa/vector_ar/tests/test_coint.py::test_coint_johansen_0lag tsa/vector_ar/tests/test_vecm.py::test_select_coint_rank /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/vecm.py:731: ComplexWarning: Casting complex values to real discards the imaginary part lr1[i] = -t * np.sum(tmp, 0) tsa/vector_ar/tests/test_coint.py::TestCointJoh12::test_basic tsa/vector_ar/tests/test_coint.py::TestCointJoh09::test_basic tsa/vector_ar/tests/test_coint.py::TestCointJohMin18::test_basic tsa/vector_ar/tests/test_coint.py::TestCointJoh25::test_basic tsa/vector_ar/tests/test_coint.py::test_coint_johansen_0lag tsa/vector_ar/tests/test_vecm.py::test_select_coint_rank /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/vecm.py:732: ComplexWarning: Casting complex values to real discards the imaginary part lr2[i] = -t * np.log(1 - a[i]) tsa/vector_ar/tests/test_var.py::test_irf_err_bands tsa/vector_ar/tests/test_var.py::test_irf_err_bands /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/irf.py:528: ComplexWarning: Casting complex values to real discards the imaginary part W[i,j,:,:], eigva[i,j,:,0], k[i,j] = util.eigval_decomp(cov_hold[i,j,:,:]) tsa/vector_ar/tests/test_var.py::test_irf_err_bands /build/python-statsmodels/src/statsmodels/build/lib.linux-riscv64-cpython-314/statsmodels/tsa/vector_ar/irf.py:483: ComplexWarning: Casting complex values to real discards the imaginary part W[i], eigva[i], k[i] = util.eigval_decomp(stack_cov[i]) -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html =========================== short test summary info ============================ FAILED statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedPoisson_predict::test_predict_prob - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedPoisson_predict::test_predict_options - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP_predict::test_predict_prob - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP_predict::test_predict_generic_zi - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/discrete/tests/test_predict.py::TestZINegativeBinomialPPredict::test_predict - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/discrete/tests/test_predict.py::TestZINegativeBinomialPPredict::test_diagnostic - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/discrete/tests/test_predict.py::test_distr[case2] - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/discrete/tests/test_predict.py::test_distr[case3] - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/discrete/tests/test_predict.py::test_distr[case4] - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/discrete/tests/test_predict.py::test_distr[case5] - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/discrete/tests/test_predict.py::test_distr[case6] - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/distributions/tests/test_discrete.py::TestZIPoisson::test_pmf_zero - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/distributions/tests/test_discrete.py::TestZIPoisson::test_logpmf_zero - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/distributions/tests/test_discrete.py::TestZIPoisson::test_pmf - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/distributions/tests/test_discrete.py::TestZIPoisson::test_logpmf - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/distributions/tests/test_discrete.py::TestZIPoisson::test_moments - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/distributions/tests/test_discrete.py::TestZIGeneralizedPoisson::test_pmf_zero - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/distributions/tests/test_discrete.py::TestZIGeneralizedPoisson::test_logpmf_zero - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/distributions/tests/test_discrete.py::TestZIGeneralizedPoisson::test_pmf - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/distributions/tests/test_discrete.py::TestZIGeneralizedPoisson::test_logpmf - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/distributions/tests/test_discrete.py::TestZiNBP::test_pmf_p2 - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/distributions/tests/test_discrete.py::TestZiNBP::test_logpmf_p2 - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/distributions/tests/test_discrete.py::TestZiNBP::test_moments_p2 - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/distributions/tests/test_discrete.py::TestZiNBP::test_pmf - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/distributions/tests/test_discrete.py::TestZiNBP::test_logpmf - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/distributions/tests/test_discrete.py::TestZiNBP::test_moments - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) FAILED statsmodels/stats/tests/test_knockoff.py::test_equi - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' FAILED statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester0-49] - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' FAILED statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester0-50] - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' FAILED statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester1-49] - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' FAILED statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester1-50] - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' FAILED statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester2-49] - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' FAILED statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester2-50] - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' FAILED statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester3-49] - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' FAILED statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester3-50] - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' FAILED statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester4-49] - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' FAILED statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester4-50] - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' FAILED statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester5-49] - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' FAILED statsmodels/stats/tests/test_knockoff.py::test_testers[equi-tester5-50] - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' FAILED statsmodels/stats/tests/test_knockoff.py::test_sim[tester0-300-100-6-equi] - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' FAILED statsmodels/stats/tests/test_knockoff.py::test_sim[tester1-300-100-3.5-equi] - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' FAILED statsmodels/stats/tests/test_knockoff.py::test_sim[tester2-300-100-3.5-equi] - numpy._core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'multiply' output from dtype('complex128') to dtype('float64') with casting rule 'same_kind' ERROR statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson_predict::test_mean - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) ERROR statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson_predict::test_var - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) ERROR statsmodels/discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson_predict::test_predict_prob - ImportError: cannot import name '_lazywhere' from 'scipy._lib._util' (/usr/lib/python3.14/site-packages/scipy/_lib/_util.py) = 42 failed, 17494 passed, 307 skipped, 140 xfailed, 2 xpassed, 3579 warnings, 3 errors in 10290.16s (2:51:30) = ==> ERROR: A failure occurred in check().  Aborting... ==> ERROR: Build failed, check /var/lib/archbuild/extra-riscv64/felix-11/build receiving incremental file list python-statsmodels-0.14.6-2-riscv64-build.log python-statsmodels-0.14.6-2-riscv64-check.log python-statsmodels-0.14.6-2-riscv64-prepare.log sent 81 bytes received 150,204 bytes 60,114.00 bytes/sec total size is 3,461,145 speedup is 23.03