Nowcasting in a pandemic using non-parametric mixed frequency VARs
Huber, Florian and Koop, Gary and Onorante, Luca and Pfarrhofer, Michael and Schreiner, Josef (2020) Nowcasting in a pandemic using non-parametric mixed frequency VARs. Journal of Econometrics, n/a. n/a. ISSN 0304-4076 (https://doi.org/10.1016/j.jeconom.2020.11.006)
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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 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.
ORCID iDs
Huber, Florian, Koop, Gary
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Item type: Article ID code: 74774 Dates: DateEvent17 December 2020Published17 December 2020Published Online28 November 2020AcceptedKeywords: regression tree models, Bayesian, macroeconomic forecasting, vector autoregression, Economic History and Conditions, History and Philosophy of Science, Economics and Econometrics, Applied Mathematics Subjects: Social Sciences > Economic History and Conditions Department: Strathclyde Business School > Economics Depositing user: Pure Administrator Date deposited: 03 Dec 2020 12:30 Last modified: 03 May 2022 00:28 URI: https://strathprints.strath.ac.uk/id/eprint/74774