Nowcasting in a pandemic using non-parametric mixed frequency VARs

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

No 2510, Working Paper Series from European Central Bank

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. JEL Classification: C11, C32, C53, E37

Keywords: Bayesian; macroeconomic forecasting; regression tree models; vector autoregressions (search for similar items in EconPapers)
Date: 2021-01
New Economics Papers: this item is included in nep-eec, nep-ets, nep-for, nep-mac and nep-ore
Note: 412615
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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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 (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20212510

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