Working Papers,
Örebro University, School of Business

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.

Keywords: Time-Varying Parameter models; High-dimensional Vector Autoregressions; Stochastic Volatility; B-splines; Macroeconomic Forecasting

JEL-codes: C11; C33; C53

Language: English

44 pages, December 2, 2025

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