期刊
JOURNAL OF ECONOMIC DYNAMICS & CONTROL
卷 70, 期 -, 页码 86-100出版社
ELSEVIER
DOI: 10.1016/j.jedc.2016.06.006
关键词
GVAR; Global economy; Forecast evaluation; Log score; Copula
类别
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. (C) 2016 Elsevier B.V. All rights reserved.
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