VAR forecasting using Bayesian variable selection

Korobilis, D. (2011) VAR forecasting using Bayesian variable selection. Journal of Applied Econometrics, 28(2), pp. 204-230. (doi: 10.1002/jae.1271)

Abstract

This paper develops methods for automatic selection of variables in Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally efficient algorithms for stochastic variable selection in generic linear and nonlinear models, as well as models of large dimensions. The performance of the proposed variable selection method is assessed in forecasting three major macroeconomic time series of the UK economy. Data-based restrictions of VAR coefficients can help improve upon their unrestricted counterparts in forecasting, and in many cases they compare favorably to shrinkage estimators.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Korompilis Magkas, Professor Dimitris
• Authors: Korobilis, D.
• Subjects: H Social Sciences > HA Statistics; H Social Sciences > HB Economic Theory
• College/School: College of Social Sciences > Adam Smith Business School > Economics
• Journal Name: Journal of Applied Econometrics
• Journal Abbr.: J. Appl. Econ.
• ISSN: 0883-7252
• ISSN (Online): 1099-1255
• Published Online: 25 October 2011