Nowcasting in a pandemic using non-parametric mixed frequency VARs

Huber, Florian and Koop, Gary and Onorante, Luca and Pfarrhofer, Michael and Schreiner, Josef (2023) Nowcasting in a pandemic using non-parametric mixed frequency VARs. Journal of Econometrics, 232 (1). pp. 52-69. ISSN 0304-4076 (https://doi.org/10.1016/j.jeconom.2020.11.006)

[thumbnail of Huber-etal-JOE-2023-Nowcasting-in-a-pandemic-using-non-parametric]
Preview
 Text. Filename: Huber_etal_JOE_2023_Nowcasting_in_a_pandemic_using_non_parametric.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (1MB)| Preview

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.

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
    31 January 2023
    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:05 Dec 2023 01:54
    URI:https://strathprints.strath.ac.uk/id/eprint/74774
CORE (COnnecting REpositories)