Prior Selection for Panel Vector Autoregressions
25 Pages Posted: 5 May 2015
Date Written: May 4, 2015
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
JEL Classification: C11, C33, C52
Suggested Citation: Suggested Citation