Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/339199 
Erscheinungsjahr: 
2025
Schriftenreihe/Nr.: 
Working Paper No. 16/2025
Verlag: 
Örebro University School of Business, Örebro
Zusammenfassung: 
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.
Schlagwörter: 
Time-Varying Parameter models
High-dimensional Vector Autoregressions
Stochastic Volatility
B-splines
Macroeconomic Forecasting
JEL: 
C11
C33
C53
Dokumentart: 
Working Paper

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