PyTimeVar: A python package for trending time-varying time series models - Mingxuan Song; Bernhard van der Sluis; Yicong Lin (Tinbergen Institute, 2024)
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.
Keywords: 330; time-varying; bootstrap; nonparametric estimation; boosted Hodrick-Prescott filter; power-law trend; score-driven; state-space