论文标题
使用非参数混合频率VAR在大流行中进行现象
Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs
论文作者
论文摘要
本文使用加性回归树开发了贝叶斯计量经济学方法，用于非参数混合频率VAR的后推断。我们认为，回归树模型非常适合面对极端观察的宏观经济象征，例如2020年COVID-19的大流行产生的观测。这是由于它们的灵活性和对异常值建模的能力。在涉及四个主要欧元区国家 /地区的应用程序中，我们发现相对于线性混合频率VAR的现象性能有了很大的改善。
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.
