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 .
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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and predictive inference in a
inference in
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VAR
VARs
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the extreme observations
extreme observations, for instance those
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pandemic
COVID-19 pandemic of 2020. This is
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a nowcasting
an
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countries in the European Union
euro area countries
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. A detailed examination of the predictive densities in the first six months of 2020 shows where these improvements are achieved