Approximate Bayesian inference and forecasting in huge-dimensional multi-country VARs

Martin Feldkircher, Florian Huber*, Gary Koop, Michael Pfarrhofer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Panel Vector Autoregressions (PVARs) are a popular tool for analyzing multi-country datasets. However, the number of estimated parameters can be enormous, leading to computational and statistical issues. In this paper, 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 while 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.
Original languageEnglish
JournalInternational Economic Review
Publication statusAccepted/In press - 7 Feb 2022

Fields of Science and Technology Classification 2012

  • 502 Economics

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