Approximate Bayesian inference and forecasting in huge-dimensional multicountry VARs

Author(s)
Martin Feldkircher, Florian Huber, Gary Koop, Michael Pfarrhofer
Abstract

Panel vector autoregressions (PVARs) are a popular tool for analyzing multicountry data sets. However, the number of estimated parameters can be enormous, leading to computational and statistical issues. In this article, we develop fast Bayesian methods for estimating PVARs using integrated rotated Gaussian approximations. We exploit the fact that domestic information is often more important than international information and group the coefficients accordingly. Fast approximations are used to estimate the latter whereas the former are estimated with precision using Markov chain Monte Carlo techniques. We illustrate, using a huge model of the world economy, that it produces competitive forecasts quickly.

Organisation(s)
External organisation(s)
Diplomatische Akademie Wien, University of Strathclyde, Paris-Lodron Universität Salzburg
Journal
International Economic Review
Volume
63
Pages
1625-1658
No. of pages
34
ISSN
0020-6598
DOI
https://doi.org/10.1111/iere.12577
Publication date
03-2022
Peer reviewed
Yes
Austrian Fields of Science 2012
502025 Econometrics, 502018 Macroeconomics
Keywords
ASJC Scopus subject areas
Economics and Econometrics
Portal url
https://ucris.univie.ac.at/portal/en/publications/approximate-bayesian-inference-and-forecasting-in-hugedimensional-multicountry-vars(b103243e-a5b2-4361-9ca8-4b791ae6dde7).html