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

Martin Feldkircher, Gregor Kastner, Florian Huber

Publication: Working/Discussion PaperWU Working Paper

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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.
Original languageEnglish
Place of PublicationVienna
PublisherWU Vienna University of Economics and Business
DOIs
Publication statusPublished - 2018

Publication series

SeriesDepartment of Economics Working Paper Series
Number260

WU Working Paper Series

  • Department of Economics Working Paper Series

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