IMF Working Papers

Predictive Density Aggregation: A Model for Global GDP Growth

Author/Editor:

Francesca Caselli ; Francesco Grigoli ; Romain Lafarguette ; Changchun Wang

Publication Date:

May 29, 2020

Electronic Access:

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Summary:

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. Speci?cally, 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 ampli?cation e?ects 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-speci?c 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.

Series:

Working Paper No. 2020/078

Subject:

Financial crises Global financial crisis of 2008-2009 Personal income tax Taxes