Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs release_ujx3khagsfehjl5zqjndxuuu4i

by Florian Huber, Gary Koop, Luca Onorante, Michael Pfarrhofer, Josef Schreiner

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2020  

Abstract

This paper develops Bayesian econometric methods for posterior and predictive inference in a non-parametric mixed frequency VAR using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of the extreme observations produced by the pandemic due to their flexibility and ability to model outliers. In a nowcasting application involving four major countries in the European Union, we find substantial improvements in nowcasting performance relative to a linear mixed frequency VAR. A detailed examination of the predictive densities in the first six months of 2020 shows where these improvements are achieved.
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Date   2020-08-28
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arXiv  2008.12706v1
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