Predictive Density Aggregation

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. 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.

Nowcasting from cross‐sectionally dependent panels

“…This paper also connects two related empirical strands of literature, namely, cross-country macroeconomic forecasting and nowcasting. Intercountry linkages have been admitted in the forecasting literature for the past few decades; see, for instance, Canova and Ciccarelli (2004), Gavin and Theodorou (2005), Garnitz et al (2019), Chen and Ranciere (2019) for panel data; Chudik et al (2016) for Global Vector Autoregression (GVAR); and Caselli et al (2020) for density forecasting. Additionally, the recent empirical nowcasting literature has also recognised the importance of international data.…”