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

Martin Feldkircher, Gregor Kastner and Florian Huber

No 260, Department of Economics Working Paper Series from WU Vienna University of Economics and Business

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)
Date: 2018-01
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Citations: View citations in EconPapers (1)

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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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