Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs

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

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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.

Date: 2020-08, Revised 2020-12
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (31)

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http://arxiv.org/pdf/2008.12706 Latest version (application/pdf)

Related works:
Journal Article: Nowcasting in a pandemic using non-parametric mixed frequency VARs (2023)
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)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2008.12706

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