Bayesian dynamic variable selection in high dimensions

Koop, G. and Korobilis, D. (2023) Bayesian dynamic variable selection in high dimensions. International Economic Review, 64(3), pp. 1047-1074. (doi: 10.1111/iere.12623)

Abstract

This paper addresses the issue of inference in time-varying parameter (TVP) regression models in the presence of many predictors and develops a novel dynamic variable selection strategy. The proposed variational Bayes dynamic variable selection (VBDVS) algorithm allows for assessing at each time period in the sample which predictors are relevant (or not) for forecasting the dependent variable. The algorithm is used to forecast inflation using over 400 macroeconomic, financial and global predictors, many of which are potentially irrelevant or short-lived. The new methodology is able to ensure parsimonious solutions to this high-dimensional estimation problem, that translate into excellent forecast performance.

• 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: International Economic Review
• Publisher: Wiley
• ISSN: 0020-6598
• ISSN (Online): 1468-2354
• Published Online: 27 December 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in International Economic Review 64(3): 1047-1074
• Publisher Policy: Reproduced under a Creative Commons License