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PyTimeVar: A Python Package for Trending Time-Varying Time Series Models

Author

Listed:
  • Mingxuan Song

    (Vrije Universiteit Amsterdam)

  • Bernhard van der Sluis

    (Erasmus University Rotterdam)

  • Yicong Lin

    (Vrije Universiteit Amsterdam and Tinbergen Institute)

Abstract

Time-varying regression models with trends are commonly used to analyze long-term tendencies and evolving relationships in data. However, statistical inference for parameter paths is challenging, and recent literature has proposed various bootstrap methods to address this issue. Despite this, no software package in any language has yet offered the recently developed tools for conducting inference in time-varying regression models. We propose PyTimeVar, a Python package that implements nonparametric estimation along with multiple new bootstrap-assisted inference methods. It provides a range of bootstrap techniques for constructing pointwise confidence intervals and simultaneous bands for parameter curves. Additionally, the package includes four widely used methods for modeling trends and time-varying relationships. This allows users to compare different approaches within a unified environment.

Suggested Citation

  • Mingxuan Song & Bernhard van der Sluis & Yicong Lin, 2024. "PyTimeVar: A Python Package for Trending Time-Varying Time Series Models," Tinbergen Institute Discussion Papers 24-060/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20240060
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    References listed on IDEAS

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    1. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
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    3. Ren, Boru & Lucey, Brian, 2023. "Herding in the Chinese renewable energy market: Evidence from a bootstrapping time-varying coefficient autoregressive model," Energy Economics, Elsevier, vol. 119(C).
    4. Xiaoye Li & Zhibiao Zhao, 2019. "A time varying approach to the stock return–inflation puzzle," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(5), pages 1509-1528, November.
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    More about this item

    Keywords

    time-varying; bootstrap; nonparametric estimation; boosted Hodrick-Prescott filter; power-law trend; score-driven; state-space;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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