Predictive Density Aggregation : A Model for Global GDP Growth

In this paper we propose a novel approach to obtain the predictive density of global GDP growth. It hinges upon a bottom-up probabilistic model that estimates and combines single countries' predictive GDP growth densities, taking into account cross-country interdependencies. Specifically,we model non-parametrically the contemporaneous interdependencies across the United States,the euro area, and China via a conditional kernel density estimation of a joint distribution. Then, we characterize the potential amplification effects stemming from other large economies in each region-also with kernel density estimations-and the reaction of all other economies with para-metric assumptions. Importantly, each economy's predictive density also depends on a set of observable country-specific factors. Finally, the use of sampling techniques allows us to aggregate individual countries' densities into a world aggregate while preserving the non-i.i.d. nature of the global GDP growth distribution. Out-of-sample metrics con?rm the accuracy of our approach