Forecasting with Global Vector Autoregressive Models: A Bayesian Approach

Jesus Crespo Cuaresma, Martin Feldkircher, Florian Huber

Publication: Scientific journalJournal articlepeer-review

Abstract

This paper develops a Bayesian variant of global vector autoregressive (B-GVAR) models to forecast an international set of macroeconomic and financial variables. We propose a set of hierarchical priors and compare the predictive performance of B-GVAR models in terms of point and density forecasts for one-quarter-ahead and four-quarter-ahead forecast horizons. We find that forecasts can be improved by employing a global framework and hierarchical priors which induce country-specific degrees of shrinkage on the coefficients of the GVAR model. Forecasts from various B-GVAR specifications tend to outperform forecasts from a naive univariate model, a global model without shrinkage on the parameters and country-specific vector autoregressions.
Original languageEnglish
Pages (from-to)1371 - 1391
JournalJournal of Applied Econometrics
Volume31
Issue number7
DOIs
Publication statusPublished - 2016

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