We analyze how modeling international dependencies improves forecasts for the global economy based on a Bayesian GVAR with SSVS prior and stochastic volatility. To analyze the source of performance gains, we decompose the predictive joint density into its marginals and a copula term capturing...

We present evidence that global vectorautoregressive (GVAR) models produce significantly more accurate recession forecasts than country-specific time-series models in a Bayesian framework. This result holds for most countries and forecast horizons as well as for several country groups.