Predictive Density Aggregation: A Model for Global GDP Growth

Francesca Caselli, Francesco Grigoli (), Romain Lafarguette and Changchun Wang

No 2020/078, IMF Working Papers from International Monetary Fund

Abstract: 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.

Keywords: WP; GDP growth; math display; Density aggregation; density evaluation; global GDP growth; predictive density; variance estimator; world GDP; growth slowdown; growth density; Global financial crisis of 2008-2009; Personal income tax; Global; growth outcome; large economy (search for similar items in EconPapers)
Pages: 33
Date: 2020-05-29
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Citations: View citations in EconPapers (2)

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