Nowcasting in a pandemic using non-parametric mixed frequency VARs

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

*Corresponding author for this work

Publication: Scientific journalJournal articlepeer-review

Abstract

This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced by the COVID-19 pandemic of 2020. This is due to their flexibility and ability to model outliers. In an application involving four major euro area countries, we find substantial improvements in nowcasting performance relative to a linear mixed frequency VAR.
Original languageEnglish
Pages (from-to)52-69
JournalJournal of Econometrics
Volume232
Issue number1
DOIs
Publication statusPublished - Jan 2023
Externally publishedYes

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