Predictive Density Aggregation : A Model for Global GDP Growth /

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 mod...

Полное описание

Библиографические подробности
Главный автор: Caselli, Francesca
Другие авторы: Grigoli, Francesco, Lafarguette, Romain, Wang, Changchun
Формат: Журнал
Язык:English
Опубликовано: Washington, D.C. : International Monetary Fund, 2020.
Серии:IMF Working Papers; Working Paper ; No. 2020/078
Предметы:
Online-ссылка:Full text available on IMF
Описание
Итог: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.
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Объем:1 online resource (33 pages)
Формат:Mode of access: Internet
ISSN:1018-5941
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