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

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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 ORCID logoORCID: https://orcid.org/0000-0002-6091-378X, Onorante, Luca, Pfarrhofer, Michael and Schreiner, Josef;
    • Item type:Article
      ID code:74774
      Dates:
      Date
      Event
      17 December 2020
      Published
      17 December 2020
      Published Online
      28 November 2020
      Accepted
      Keywords: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:04 Apr 2021 00:53
      URI:https://strathprints.strath.ac.uk/id/eprint/74774
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