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

Feldkircher, Martin and Huber, Florian and Kastner, Gregor (2018) Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs? Department of Economics Working Paper Series, 260. WU Vienna University of Economics and Business, Vienna.

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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.

Item Type: Paper
Keywords: global-local shrinkage priors / density predictions / hierarchical modeling / stochastic volatility / dynamic model selection
Classification Codes: JEL C11, C30, C53, E52
Divisions: Departments > Volkswirtschaft
Depositing User: Claudia Tering-Raunig
Date Deposited: 25 Jan 2018 11:42
Last Modified: 26 Apr 2018 15:14
URI: http://epub.wu.ac.at/id/eprint/6021

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