Jesus Crespo Cuaresma
Martin Feldkircher
Florian Huber

forecasting with global vector autoregressive models: a bayesian approach (replication data)

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.

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Suggested Citation

Cuaresma, Jesus Crespo; Feldkircher, Martin; Huber, Florian (2016): Forecasting with Global Vector Autoregressive Models: a Bayesian Approach (replication data). Version: 1. Journal of Applied Econometrics. Dataset.