# PyTimeVar: A Python Package for Trending Time-Varying Time Series Models
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Authors: Mingxuan Song (m3.song@student.vu.nl, Vrije Universiteit Amsterdam), Bernhard van der Sluis (vandersluis@ese.eur.nl, Erasmus Universiteit Rotterdam), and Yicong Lin (yc.lin@vu.nl, Vrije Universiteit Amsterdam & Tinbergen Institute)
Discussion paper titled "PyTimeVar: A Python Package for Trending Time-Varying Time Series Models" is available at https://tinbergen.nl/discussion-paper/6365/24-060-iii-pytimevar-a-python-package-for-trending-time-varying-time-series-models.
## Purpose of the package
The PyTimeVar package offers state-of-the-art estimation and statistical inference methods for time series regression models with flexible trends and/or time-varying coefficients. The package implements nonparametric estimation along with multiple recently proposed bootstrap-assisted inference methods. Pointwise confidence intervals and simultaneous bands of parameter curves via bootstrap can be easily obtained using user-friendly commands. The package also includes four commonly used methods for modeling trends and time-varying relationships: boosted Hodrick-Prescot filter, power-law trend models, state-space models, and score-driven models. This allows users to compare different approaches within a unified environment.
The package is built upon several papers and books. We list the key references below.
### Local linear kernel estimation and bootstrap inference
Friedrich and Lin (2024) (doi: https://doi.org/10.1016/j.jeconom.2022.09.004);
Lin et al. (2025) (doi: https://doi.org/10.1080/10618600.2024.2403705);
Friedrich et al. (2020) (doi: https://doi.org/10.1016/j.jeconom.2019.05.006);
Smeekes and Urbain (2014) (doi: https://doi.org/10.26481/umagsb.2014008)
Zhou and Wu (2010) (doi: https://doi.org/10.1111/j.1467-9868.2010.00743.x);
Buhlmann (1998) (doi: https://doi.org/10.1214/aos/1030563978);
### Boosted HP filter
Mei et al. (2024) (doi: https://doi.org/10.1002/jae.3086);
Biswas et al. (2024) (doi: https://doi.org/10.1080/07474938.2024.2380704);
Phillips and Shi (2021) (doi: https://doi.org/10.1111/iere.12495);
### Power-law trend models
Lin and Reuvers (2025) (doi: https://doi.org/10.1111/jtsa.12805);
Robinson (2012) (doi: https://doi.org/10.3150/10-BEJ349);
### State-space models
Durbin and Koopman (2012) (doi: https://doi.org/10.1093/acprof:oso/9780199641178.001.0001);
### Score-driven models
Creal et al. (2013) (doi: https://doi.org/10.1002/jae.1279);
Harvey (2013) (doi: https://doi.org/10.1017/CBO9781139540933);
Harvey and Luati (2014) (doi: https://doi.org/10.1080/01621459.2014.887011)
Blasques et al. (2016) (doi: https://doi.org/10.1016/j.ijforecast.2015.11.018);
## Features
- Nonparametric estimation of time-varying time series models, along with various bootstrap-assisted methods for inference, including local blockwise wild bootstrap, wild bootstrap, sieve bootstrap, sieve wild bootstrap, autoregressive wild bootstrap.
- Alternative estimation methods for modeling trend and time-varying relationships, including boosted HP filter, power-law trend models, state-space, and score-driven models. The package includes inference methods for power-law trend models, state-space models, and score-driven models.
- Unified framework for comparison of methods.
- Multiple datasets for illustration.
## Getting started
The PyTimeVar can implemented as a PyPI package. To download the package in your Python environment, use the following command:
```python
pip install PyTimeVar
```
## Support
The documentation of the package can be found at the GitHub repository https://github.com/bpvand/PyTimeVar, and ReadTheDocs https://pytimevar.readthedocs.io/en/latest/.
For any questions or feedback regarding the PyTimeVar package, please feel free to contact the authors via email:
m3.song@student.vu.nl;
vandersluis@ese.eur.nl;
yc.lin@vu.nl.
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"description": "# PyTimeVar: A Python Package for Trending Time-Varying Time Series Models\r\n<!-- badges: start -->\r\n[](https://www.gnu.org/licenses/gpl-3.0)\r\n\r\n\r\n<!-- badges: end -->\r\n\r\nAuthors: Mingxuan Song (m3.song@student.vu.nl, Vrije Universiteit Amsterdam), Bernhard van der Sluis (vandersluis@ese.eur.nl, Erasmus Universiteit Rotterdam), and Yicong Lin (yc.lin@vu.nl, Vrije Universiteit Amsterdam & Tinbergen Institute)\r\n\r\nDiscussion paper titled \"PyTimeVar: A Python Package for Trending Time-Varying Time Series Models\" is available at https://tinbergen.nl/discussion-paper/6365/24-060-iii-pytimevar-a-python-package-for-trending-time-varying-time-series-models. \r\n\r\n## Purpose of the package\r\n\r\nThe PyTimeVar package offers state-of-the-art estimation and statistical inference methods for time series regression models with flexible trends and/or time-varying coefficients. The package implements nonparametric estimation along with multiple recently proposed bootstrap-assisted inference methods. Pointwise confidence intervals and simultaneous bands of parameter curves via bootstrap can be easily obtained using user-friendly commands. The package also includes four commonly used methods for modeling trends and time-varying relationships: boosted Hodrick-Prescot filter, power-law trend models, state-space models, and score-driven models. This allows users to compare different approaches within a unified environment.\r\n\r\nThe package is built upon several papers and books. We list the key references below.\r\n\r\n### Local linear kernel estimation and bootstrap inference\r\nFriedrich and Lin (2024) (doi: https://doi.org/10.1016/j.jeconom.2022.09.004);\r\nLin et al. (2025) (doi: https://doi.org/10.1080/10618600.2024.2403705);\r\nFriedrich et al. (2020) (doi: https://doi.org/10.1016/j.jeconom.2019.05.006);\r\nSmeekes and Urbain (2014) (doi: https://doi.org/10.26481/umagsb.2014008)\r\nZhou and Wu (2010) (doi: https://doi.org/10.1111/j.1467-9868.2010.00743.x);\r\nBuhlmann (1998) (doi: https://doi.org/10.1214/aos/1030563978);\r\n\r\n\r\n### Boosted HP filter\r\nMei et al. (2024) (doi: https://doi.org/10.1002/jae.3086);\r\nBiswas et al. (2024) (doi: https://doi.org/10.1080/07474938.2024.2380704);\r\nPhillips and Shi (2021) (doi: https://doi.org/10.1111/iere.12495);\r\n\r\n\r\n### Power-law trend models\r\nLin and Reuvers (2025) (doi: https://doi.org/10.1111/jtsa.12805);\r\nRobinson (2012) (doi: https://doi.org/10.3150/10-BEJ349);\r\n\r\n\r\n### State-space models\r\nDurbin and Koopman (2012) (doi: https://doi.org/10.1093/acprof:oso/9780199641178.001.0001);\r\n\r\n### Score-driven models\r\nCreal et al. (2013) (doi: https://doi.org/10.1002/jae.1279);\r\nHarvey (2013) (doi: https://doi.org/10.1017/CBO9781139540933);\r\nHarvey and Luati (2014) (doi: https://doi.org/10.1080/01621459.2014.887011)\r\nBlasques et al. (2016) (doi: https://doi.org/10.1016/j.ijforecast.2015.11.018);\r\n\r\n## Features\r\n\r\n- Nonparametric estimation of time-varying time series models, along with various bootstrap-assisted methods for inference, including local blockwise wild bootstrap, wild bootstrap, sieve bootstrap, sieve wild bootstrap, autoregressive wild bootstrap.\r\n- Alternative estimation methods for modeling trend and time-varying relationships, including boosted HP filter, power-law trend models, state-space, and score-driven models. The package includes inference methods for power-law trend models, state-space models, and score-driven models.\r\n- Unified framework for comparison of methods.\r\n- Multiple datasets for illustration.\r\n\r\n## Getting started\r\n\r\nThe PyTimeVar can implemented as a PyPI package. To download the package in your Python environment, use the following command:\r\n```python\r\npip install PyTimeVar\r\n```\r\n\r\n## Support\r\nThe documentation of the package can be found at the GitHub repository https://github.com/bpvand/PyTimeVar, and ReadTheDocs https://pytimevar.readthedocs.io/en/latest/.\r\n\r\nFor any questions or feedback regarding the PyTimeVar package, please feel free to contact the authors via email: \r\nm3.song@student.vu.nl; \r\nvandersluis@ese.eur.nl; \r\nyc.lin@vu.nl.\r\n",
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