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

7 Citations (Scopus)
20 Downloads (Pure)


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
Number of pages18
JournalJournal of Econometrics
Issue number1
Early online date17 Dec 2020
Publication statusPublished - 31 Jan 2023


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


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