Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs?

Martin Feldkircher, Florian Huber and Gregor Kastner

Department of Economics Working Papers from Vienna University of Economics and Business, Department of Economics

Abstract: We assess the relationship between model size and complexity in the time-varying parameter VAR framework via thorough predictive exercises for the Euro Area, the United Kingdom and the United States. It turns out that sophisticated dynamics through drifting coefficients are important in small data sets while simpler models tend to perform better in sizeable data sets. To combine best of both worlds, novel shrinkage priors help to mitigate the curse of dimensionality, resulting in competitive forecasts for all scenarios considered. Furthermore, we discuss dynamic model selection to improve upon the best performing individual model for each point in time.

Keywords: Global-local shrinkage priors; density predictions; hierarchical modeling; stochastic volatility; dynamic model selection (search for similar items in EconPapers)
JEL-codes: C11 C30 C53 E52 (search for similar items in EconPapers)
Date: 2018-01
New Economics Papers: this item is included in nep-ecm and nep-mac
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Related works:
Working Paper: Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs? (2024)
Working Paper: Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs? (2018)
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