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Moderate Time-Varying Parameter VARs

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Abstract

This paper proposes a parsimonious reparametrization for time-varying parameter models that captures smooth dynamics through a low-dimensional state process combined with B-spline weights. We apply this framework to TVP-VARs, yielding Moderate TVP-VARs that retain the interpretability of standard specifications while mitigating overfitting. Monte Carlo evidence shows faster estimation, lower bias, and strong robustness to knot placement. In U.S. macroeconomic data, moderate specifications recover meaningful long-run movements, produce stable impulse responses and deliver superior density forecasts and predictive marginal likelihoods relative to conventional TVP-VARs, particularly in high-dimensional settings.

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  • Celani, Alessandro & Pedini, Luca, 2025. "Moderate Time-Varying Parameter VARs," Working Papers 2025:16, Örebro University, School of Business.
    • Handle: RePEc:hhs:oruesi:2025_016
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    1. Zheng, Tingguo & Ye, Shiqi & Hong, Yongmiao, 2023. "Fast estimation of a large TVP-VAR model with score-driven volatilities," Journal of Economic Dynamics and Control, Elsevier, vol. 157(C).
    2. Bitto, Angela & Frühwirth-Schnatter, Sylvia, 2019. "Achieving shrinkage in a time-varying parameter model framework," Journal of Econometrics, Elsevier, vol. 210(1), pages 75-97.
    3. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2024. "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," The Review of Economics and Statistics, MIT Press, vol. 106(5), pages 1403-1417, September.
    4. Petrova, Katerina, 2019. "A quasi-Bayesian local likelihood approach to time varying parameter VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 286-306.
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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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