Nowcasting in a pandemic using non-parametric mixed frequency VARs
Journal of Econometrics
; 232(1):52-69, 2023.
Article
in English
| Scopus | ID: covidwho-2241596
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. © 2020 The Author(s)
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Journal of Econometrics
Year:
2023
Document Type:
Article
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