Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs

2020/08/28 by Florian Huber, Huber, Florian, Gary Koop +7 · 3 citations
Economics, Econometrics and Finance · Mathematics · #Applications (stat.AP) #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Italy: Economic History and Contemporary Issues #Machine Learning (stat.ML) #Market Dynamics and Volatility #Monetary Policy and Economic Impact #econ.EM #stat.AP #stat.ML

paper · pdf · doi:10.48550/arxiv.2008.12706

JEL: C11, C32, C53, E37; Keywords: Regression tree models, Bayesian, macroeconomic forecasting, vector autoregressions

openalex publication_date 2020/08/28 · arxiv created 2020/12/01 · arxiv updated 2020/12/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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