Arbeitspapier

Nowcasting in a pandemic using non-parametric mixed frequency VARs

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.

ISBN
978-92-899-4509-7
Sprache
Englisch

Erschienen in
Series: ECB Working Paper ; No. 2510

Klassifikation
Wirtschaft
Bayesian Analysis: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Forecasting Models; Simulation Methods
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
Thema
Regression tree models
Bayesian
macroeconomic forecasting
vector autoregressions

Ereignis
Geistige Schöpfung
(wer)
Huber, Florian
Koop, Gary
Onorante, Luca
Pfarrhofer, Michael
Schreiner, Josef
Ereignis
Veröffentlichung
(wer)
European Central Bank (ECB)
(wo)
Frankfurt a. M.
(wann)
2021

DOI
doi:10.2866/262195
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Huber, Florian
  • Koop, Gary
  • Onorante, Luca
  • Pfarrhofer, Michael
  • Schreiner, Josef
  • European Central Bank (ECB)

Entstanden

  • 2021

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