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.
翻译:本文开发了贝叶西亚经济计量方法,用于利用添加回归树在非参数混合频率VARs中进行后推推。我们认为,回归树模型在极端观察(例如2020年COVID-19大流行造成的观察)中最适合于现在的宏观经济预测。这是因为这些模型具有灵活性和建模超值的能力。在涉及四个欧元区国家的一项应用中,我们发现现在的预测与线性混合频率VAR相比,其性能显著改善。