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.

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...