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Predictive Density Aggregation: A Model for Global GDP Growth

Francesca G CaselliMr.Francesco GrigoliRomain LafarguetteChangchun Wang

出版	International Monetary Fund, 2020-05-29
主題	Business & Economics / Economics / MacroeconomicsBusiness & Economics / Personal Finance / Taxation
ISBN	15135456559781513545653
URL	http://books.google.com.hk/books?id=2bAaEAAAQBAJ&hl=&source=gbs_api
EBook	SAMPLE

註釋

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.