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"... Abstract This paper puts forward a Bayesian Global Vector Autoregressive Model with Common Stochastic Volatility (B-GVAR-CSV). We assume that country specific volatility is driven by a single latent stochastic process, which simplifies the analysis and implies significant computational gains. Apart ..."

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Abstract This paper puts forward a Bayesian Global Vector Autoregressive Model with Common Stochastic Volatility (B-GVAR-CSV). We assume that country specific volatility is driven by a single latent stochastic process, which simplifies the analysis and implies significant computational gains. Apart from computational advantages, this is also justified on the ground that the volatility of most macroeconomic quantities considered in our application tends to follow a similar pattern. Furthermore, Minnesota priors are used to introduce shrinkage to cure the curse of dimensionality. Finally, this model is then used to produce predictive densities for a set of macroeconomic aggregates. The dataset employed consists of quarterly data spanning from 1979:Q1 to 2013:Q4 and includes 36 economies. Our results indicate that allowing for stochastic volatility influences the accuracy along two dimensions: First, it helps to increase the overall predictive fit of our model. This result can be seen for all variables under scrutiny, most notably for real GDP, inflation, exchange rates and equity prices. Second, it helps to make the model more resilient with respect to outliers and economic crises. This implies that when evaluated over time, the log predictive scores tend to show significantly less variation as compared to homoscedastic models. JEL Classification: C32, F44, E32, E47.