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Prior selection for panel vector autoregressions

Dimitris Korobilis

No 2015-73, SIRE Discussion Papers from Scottish Institute for Research in Economics (SIRE)

Abstract: There is a vast literature that specifies 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 different 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)
Date: 2015-04-29
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http://hdl.handle.net/10943/682

Related works:
Journal Article: Prior selection for panel vector autoregressions (2016) Downloads
Working Paper: Prior selection for panel vector autoregressions (2015) Downloads
Working Paper: Prior selection for panel vector autoregressions (2015) Downloads
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