Bayesian compressed vector autoregressions

Koop, G., Korobilis, D. and Pettenuzzo, D. (2019) Bayesian compressed vector autoregressions. Journal of Econometrics, 210(1), pp. 135-154. (doi: 10.1016/j.jeconom.2018.11.009)

Abstract

Macroeconomists are increasingly working with large Vector Autoregressions (VARs) where the number of parameters vastly exceeds the number of observations. Existing approaches either involve prior shrinkage or the use of factor methods. In this paper, we develop an alternative based on ideas from the compressed regression literature. It involves randomly compressing the explanatory variables prior to analysis. A huge dimensional problem is thus turned into a much smaller, more computationally tractable one. Bayesian model averaging can be done over various compressions, attaching greater weight to compressions which forecast well. In a macroeconomic application involving up to 129 variables, we find compressed VAR\ methods to forecast as well or better than either factor methods or large VAR methods involving prior shrinkage.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Korompilis Magkas, Professor Dimitris
• Authors: Koop, G., Korobilis, D., and Pettenuzzo, 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 Econometrics
• Publisher: Elsevier
• ISSN: 0304-4076
• ISSN (Online): 1872-6895
• Published Online: 12 November 2018
• Copyright Holders: Copyright © 2018 Elsevier B.V.
• First Published: First published in Journal of Econometrics 210(1): 135-154
• Publisher Policy: Reproduced in accordance with the publisher copyright policy