Prior Selection for Panel Vector Autoregressions

25 Pages Posted: 5 May 2015

See all articles by Dimitris Korobilis

Dimitris Korobilis

University of Glasgow - Adam Smith Business School

Date Written: May 4, 2015

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

JEL Classification: C11, C33, C52

Suggested Citation

Korobilis, Dimitris, Prior Selection for Panel Vector Autoregressions (May 4, 2015). Available at SSRN: https://ssrn.com/abstract=2602508 or http://dx.doi.org/10.2139/ssrn.2602508

Dimitris Korobilis (Contact Author)

University of Glasgow - Adam Smith Business School ( email )

40 University Avenue
Gilbert Scott Building
Glasgow, Scotland G12 8QQ
United Kingdom

HOME PAGE: http://https://sites.google.com/site/dimitriskorobilis/

Register to save articles to
your library

Register

Paper statistics

Downloads
34
Abstract Views
251
PlumX Metrics