Nowcasting in a pandemic using non-parametric mixed frequency VARs

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

Journal of Econometrics, 2023, vol. 232, issue 1, 52-69

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.

Keywords: Regression tree models; Bayesian; Macroeconomic forecasting; Vector autoregressions (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 E37 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Related works:
Working Paper: Nowcasting in a pandemic using non-parametric mixed frequency VARs (2021)
Working Paper: Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs (2021)
Working Paper: Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:232:y:2023:i:1:p:52-69

DOI: 10.1016/j.jeconom.2020.11.006

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