Nowcasting in a pandemic using non-parametric mixed frequency VARs

Authors: Huber, Florian;

Koop, Gary;

Onorante, Luca;

Pfarrhofer, Michael ;

Schreiner, Josef

Source: Journal of Econometrics

Full text

: https://doi.org/10.1016/j.jeconom.2020.11.006

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.

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https://creativecommons.org/licenses/by/4.0/

Cite as

Huber, F., Koop, G., Onorante, L., Pfarrhofer, M. & Schreiner, J. 2020, 'Nowcasting in a pandemic using non-parametric mixed frequency VARs', Journal of Econometrics. https://doi.org/10.1016/j.jeconom.2020.11.006

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DOI: https://doi.org/10.1016/j.jeconom.2020.11.006

Repository URI: https://pureportal.strath.ac.uk/en/publications/nowcasting-in-a-pandemic-using-non-parametric-mixed-frequency-var