Nowcasting in a pandemic using non-parametric mixed frequency VARs
开发了基于加法回归树的贝叶斯非参数混合频率VAR方法，用于在极端观测（如新冠疫情）下进行宏观经济即时预测，并在欧元区四国的应用中显著优于线性模型。
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.