How to estimate a vector autoregression after March 2020
说明在估计宏观经济学中最常用的时间序列模型：向量自回归时，如何处理COVID-19疫情期间出现的极端观测值，发现删除这些观测值对参数估计可行，但用于预测会低估不确定性。
Summary This paper illustrates how to handle a sequence of extreme observations—such as those recorded during the COVID‐19 pandemic—when estimating a vector autoregression, which is the most popular time‐series model in macroeconomics. Our results show that the ad hoc strategy of dropping these observations may be acceptable for the purpose of parameter estimation. However, disregarding these recent data is inappropriate for forecasting the future evolution of the economy, because it may underestimate uncertainty.