Forecasting with Global Vector Autoregressive Models: a Bayesian Approach

Authors

  • Jesús Crespo Cuaresma,

    Corresponding author
    1. Vienna University of Economics and Business (WU), Austria
    2. Wittgenstein Centre for Demography and Human Capital (WIC), Vienna, Austria
    3. International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
    4. Austrian Institute of Economic Research (WIFO), Vienna, Austria
    • Correspondence to: Jesus Crespo Cuaresma, Vienna University of Economics and Business (WU), Austria.

      E-mail: jcrespo@wu.ac.at

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  • Martin Feldkircher,

    1. Oesterreichische Nationalbank (OeNB), Vienna, Austria
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  • Florian Huber

    1. Vienna University of Economics and Business (WU), Austria
    2. Oesterreichische Nationalbank (OeNB), Vienna, Austria
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Summary

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. Copyright © 2016 John Wiley & Sons, Ltd.

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