Nowcasting in a pandemic using non-parametric mixed frequency VARs

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

Research output: Contribution to journalArticlepeer-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 exibility 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)n/a
Number of pages39
JournalJournal of Econometrics
Volumen/a
Early online date17 Dec 2020
DOIs
Publication statusE-pub ahead of print - 17 Dec 2020

Keywords

  • regression tree models
  • Bayesian
  • macroeconomic forecasting
  • vector autoregression

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