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We propose a set of hierarchical priors and compare the predictive performance of B\u2010GVAR models in terms of point and density forecasts for one\u2010quarter\u2010ahead and four\u2010quarter\u2010ahead forecast horizons. We find that forecasts can be improved by employing a global framework and hierarchical priors which induce country\u2010specific degrees of shrinkage on the coefficients of the GVAR model. Forecasts from various B\u2010GVAR specifications tend to outperform forecasts from a naive univariate model, a global model without shrinkage on the parameters and country\u2010specific vector autoregressions. 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