Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs

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

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in FachzeitschriftArtikelPeer-reviewed

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.
OriginalspracheEnglisch
FachzeitschriftJournal of Econometrics
DOIs
PublikationsstatusElektronische Veröffentlichung vor Drucklegung - Dez 2020

Systematik der Wissenschaftszweige 2012

  • 502 Wirtschaftswissenschaften

Zitieren