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Does joint modelling of the world economy pay off? Evaluating global forecasts from a Bayesian GVAR. (English) Zbl 1401.91490

Summary: 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 the dependence structure across countries. The GVAR outperforms forecasts based on country-specific models. This performance is solely driven by superior predictions for the dependence structure across countries, whereas the GVAR does not yield better predictive marginal densities. The relative performance gains of the GVAR model are particularly pronounced during volatile periods and for emerging economies.

MSC:

91B82 Statistical methods; economic indices and measures
62P20 Applications of statistics to economics
62F15 Bayesian inference
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
91B64 Macroeconomic theory (monetary models, models of taxation)
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