Does Joint Modelling of the World Economy Pay Off? Evaluating Multivariate Forecasts from a Bayesian GVAR

Jonas Dovern, Martin Feldkircher and Florian Huber

VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy from Verein für Socialpolitik / German Economic Association

Abstract: To assess the performance of multivariate density forecasts for the world economy based on a Bayesian global vector autoregressive (GVAR) model, we decompose the predictive joint density into its marginals and a copula term that captures the dependence structure among variables and countries. Moreover, we use the stochastic search variable selection prior (SSVS) on the coefficients in its conjugate form to account for model uncertainty at the national level and augment the GVAR framework to allow for stochastic volatility. Our results are as follows: First, the GVAR systematically outperforms forecasts based on country-specific models in terms of predictive joint density. Second, the good GVAR performance is driven by superior predictions for the dependence structure across variables, whereas the GVAR model does not yield better predictive marginal densities. Third, the relative performance gains of the GVAR model are particularly pronounced during the Great Recession. Finally, our results imply that for some countries a more parsimonious GVAR model can further improve the forecast quality.

JEL-codes: C53 E37 F41 (search for similar items in EconPapers)
Date: 2015
New Economics Papers: this item is included in nep-for and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:vfsc15:112999

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