Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs
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by
Florian Huber, Gary Koop, Luca Onorante, Michael Pfarrhofer, Josef Schreiner
2020
Abstract
This paper develops Bayesian econometric methods for posterior and predictive
inference in a non-parametric mixed frequency VAR using additive regression
trees. We argue that regression tree models are ideally suited for
macroeconomic nowcasting in the face of the extreme observations produced by
the pandemic due to their flexibility and ability to model outliers. In a
nowcasting application involving four major countries in the European Union, we
find substantial improvements in nowcasting performance relative to a linear
mixed frequency VAR. A detailed examination of the predictive densities in the
first six months of 2020 shows where these improvements are achieved.
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