Predictive Density Aggregation: A Model for Global GDP Growth

34 Pages Posted: 30 Jun 2020

See all articles by Francesca Caselli

Francesca Caselli

International Monetary Fund (IMF)

Francesco Grigoli

International Monetary Fund (IMF)

Romain Lafarguette

International Monetary Fund; European Central Bank (ECB)

Changchun Wang

International Monetary Fund (IMF)

Date Written: May 1, 2020

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. 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.

Keywords: Real effective exchange rates, Indicators of economic activity, Economic growth, Economic indicators, Economic policy, Density aggregation, density evaluation, global GDP growth, predictive density., WP, euro area, GDP growth, country-specific, joint density, regional leader

JEL Classification: C12, E17, E37, E01, G21, C43, H61, O24

Suggested Citation

Caselli, Francesca and Grigoli, Francesco and Lafarguette, Romain and Wang, Changchun, Predictive Density Aggregation: A Model for Global GDP Growth (May 1, 2020). IMF Working Paper No. 20/78, Available at SSRN: https://ssrn.com/abstract=3638525

Francesca Caselli (Contact Author)

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

Francesco Grigoli

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

Romain Lafarguette

International Monetary Fund ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

European Central Bank (ECB) ( email )

Sonnemannstrasse 22
Frankfurt am Main, 60314
Germany

Changchun Wang

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

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