Forecasting with high-dimensional panel VARs

Koop, G. and Korobilis, D. (2019) Forecasting with high-dimensional panel VARs. Oxford Bulletin of Economics and Statistics, 81(5), pp. 937-959. (doi: 10.1111/obes.12303)

Abstract

This paper develops methods for estimating and forecasting in Bayesian panel vector autoregressions of large dimensions with time‐varying parameters and stochastic volatility. We exploit a hierarchical prior that takes into account possible pooling restrictions involving both VAR coefficients and the error covariance matrix, and propose a Bayesian dynamic learning procedure that controls for various sources of model uncertainty. We tackle computational concerns by means of a simulation‐free algorithm that relies on analytical approximations to the posterior. We use our methods to forecast inflation rates in the eurozone and show that these forecasts are superior to alternative methods for large vector autoregressions.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Korompilis Magkas, Professor Dimitris
• Authors: Koop, G., and Korobilis, D.
• College/School: College of Social Sciences > Adam Smith Business School > Economics
• Journal Name: Oxford Bulletin of Economics and Statistics
• Publisher: Wiley
• ISSN: 0305-9049
• ISSN (Online): 1468-0084
• Published Online: 28 February 2019
• Copyright Holders: Copyright © 2019 The Department of Economics, University of Oxford and John Wiley and Sons Ltd.
• First Published: First published in Oxford Bulletin of Economics and Statistics 81(5):937-959
• Publisher Policy: Reproduced in accordance with the publisher copyright policy