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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 September 19, 2024

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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.

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:

    ---

  • Issue:

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  • Series:

    Working Paper No. 2020/078

  • Stock No:

    WPIEA2020078

  • ISBN:

    9781513545653

  • ISSN:

    1018-5941