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

• URL: http://arxiv.org/abs/2103.04944v1
• Date: Mon, 8 Mar 2021 18:02:59 GMT
• Title: Approximate Bayesian inference and forecasting in huge-dimensional multi-country VARs
• Authors: Martin Feldkircher, Florian Huber, Gary Koop, Michael Pfarrhofer
• Abstract summary: The Panel Vector Autoregressive model is a popular tool for macroeconomic forecasting and structural analysis. It allows for spillovers between countries in a very flexible fashion. The number of parameters to be estimated can be enormous leading to over- parameterization concerns.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Panel Vector Autoregressive (PVAR) model is a popular tool for macroeconomic forecasting and structural analysis in multi-country applications since it allows for spillovers between countries in a very flexible fashion. However, this flexibility means that the number of parameters to be estimated can be enormous leading to over-parameterization concerns. Bayesian global-local shrinkage priors, such as the Horseshoe prior used in this paper, can overcome these concerns, but they require the use of Markov Chain Monte Carlo (MCMC) methods rendering them computationally infeasible in high dimensions. In this paper, we develop computationally efficient Bayesian methods for estimating PVARs using an integrated rotated Gaussian approximation (IRGA). This exploits the fact that whereas own country information is often important in PVARs, information on other countries is often unimportant. Using an IRGA, we split the the posterior into two parts: one involving own country coefficients, the other involving other country coefficients. Fast methods such as approximate message passing or variational Bayes can be used on the latter and, conditional on these, the former are estimated with precision using MCMC methods. In a forecasting exercise involving PVARs with up to $18$ variables for each of $38$ countries, we demonstrate that our methods produce good forecasts quickly.

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