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

Martin Feldkircher, Luis Gruber, Florian Huber and Gregor Kastner

Papers from arXiv.org

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 the 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.

Date: 2017-11, Revised 2024-03
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Citations: View citations in EconPapers (18)

Published in Journal of Forecasting (2024)

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http://arxiv.org/pdf/1711.00564 Latest version (application/pdf)

Related works:
Working Paper: Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs? (2018)
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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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1711.00564

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