Nowcasting in a pandemic using non-parametric mixed frequency VARs

Arbeitspapier

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.

Angaben zum Objekt

• ISBN: 978-92-76-28772-8
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: JRC Working Papers in Economics and Finance ; No. 2021/1

Klassifikation und Themen

• 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

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Huber, Florian ; Koop, Gary ; Onorante, Luca ; Pfarrhofer, Michael ; Schreiner, Josef
• Ereignis: Veröffentlichung
• (wer): Publications Office of the European Union
• (wo): Luxembourg
• (wann): 2021

Weitere Informationen

• DOI: doi:10.2760/197767
• Handle: http://hdl.handle.net/10419/249363
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ