Predictive Density Aggregation: A Model for Global GDP Growth

By Francesca Caselli, Francesco Grigoli, Romain Lafarguette, Changchun Wang

May 29, 2020

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Francesca Caselli, Francesco Grigoli, Romain Lafarguette, and Changchun Wang. Predictive Density Aggregation: A Model for Global GDP Growth, (USA: International Monetary Fund, 2020) accessed December 21, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

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.

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

Keywords: Density aggregation, Density evaluation, GDP growth, Global, Global financial crisis of 2008-2009, Global GDP growth, Growth density, Growth outcome, Growth slowdown, Large economy, Math display, Personal income tax, Predictive density, Variance estimator, World GDP, WP

Publication Details

Pages:

33
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2020/078
Stock No:

WPIEA2020078
ISBN:

9781513545653
ISSN:

1018-5941

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