Forecasting with Global Vector Autoregressive Models: a Bayesian Approach

Crespo Cuaresma, J., Feldkircher, M., & Huber, F. (2016). Forecasting with Global Vector Autoregressive Models: a Bayesian Approach. Journal of Applied Econometrics 31 (7) 1371-1391. 10.1002/jae.2504.

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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 predicive 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.

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