Adaptive hierarchical priors for high-dimensional vector autoregressions

Korobilis, D. and Pettenuzzo, D. (2019) Adaptive hierarchical priors for high-dimensional vector autoregressions. Journal of Econometrics, 212(1), pp. 241-271. (doi: 10.1016/j.jeconom.2019.04.029)

Abstract

This paper proposes a simulation-free estimation algorithm for vector autoregressions (VARs) that allows fast approximate calculation of marginal parameter posterior distributions. We apply the algorithm to derive analytical expressions for independent VAR priors that admit a hierarchical representation and which would typically require computationally intensive posterior simulation methods. The benefits of the new algorithm are explored using three quantitative exercises. First, a Monte Carlo experiment illustrates the accuracy and computational gains of the proposed estimation algorithm and priors. Second, a forecasting exercise involving VARs estimated on macroeconomic data demonstrates the ability of hierarchical shrinkage priors to find useful parsimonious representations. We also show how our approach can be used for structural analysis and that it can successfully replicate important features of news-driven business cycles predicted by a large-scale theoretical model.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Korompilis Magkas, Professor Dimitris
• Authors: Korobilis, D., and Pettenuzzo, D.
• 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: 14 April 2019
• Copyright Holders: Copyright © 2019 Elsevier B.V.
• First Published: First published in Journal of Econometrics 212:241-271
• Publisher Policy: Reproduced in accordance with the publisher copyright policy