TY - DATA T1 - forecasting-with-global-vector-autoregressive-models-a-bayesian-approach AU - Cuaresma, Jesus Crespo AU - Feldkircher, Martin AU - Huber, Florian DO - doi:10.15456/jae.2022326.0700905383 AB - 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. ET - 1 PY - 2016 PB - ZBW - Leibniz Informationszentrum Wirtschaft UR - https://journaldata.zbw.eu/dataset/forecasting-with-global-vector-autoregressive-models-a-bayesian-approach ER -