Prior selection for panel vector autoregressions

Dimitris Korobilis

Working Papers from Business School - Economics, University of Glasgow

Abstract: There is a vast literature that speciÖes Bayesian shrinkage priors for vector autoregressions (VARs) of possibly large dimensions. In this paper I argue that many of these priors are not appropriate for multi-country settings, which motivates me to develop priors for panel VARs (PVARs). The parametric and semi-parametric priors I suggest not only perform valuable shrinkage in large dimensions, but also allow for soft clustering of variables or countries which are homogeneous. I discuss the implications of these new priors for modelling interdependencies and heterogeneities among di§erent countries in a panel VAR setting. Monte Carlo evidence and an empirical forecasting exercise show clear and important gains of the new priors compared to existing popular priors for VARs and PVARs.

Keywords: Bayesian model selection; shrinkage; spike and slab priors; forecasting; large vector autoregression (search for similar items in EconPapers)
JEL-codes: C11 C33 C52 (search for similar items in EconPapers)
Date: 2015-04
New Economics Papers: this item is included in nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Prior selection for panel vector autoregressions (2016)
Working Paper: Prior selection for panel vector autoregressions (2015)
Working Paper: Prior selection for panel vector autoregressions (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:gla:glaewp:2015_10

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