  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.
