On reduced form estimation of the effect of policy interventions on the COVID-19 pandemic
指出，许多不同的SIR模型都能产生相似的早期病例报告动态，并导致相同的简化形式估计，因此政策效果无法唯一确定，且面板数据双向固定效应估计可能符号错误。
Summary Several studies have estimated the effects of various nonpharmaceutical interventions on the COVID-19 pandemic using a ‘reduced form’ approach. In this paper, I show that many different SIR models can generate virtually identical dynamics of the number of reported cases during the early stages of the epidemic and lead to the same reduced form estimates. In some of these models, policy interventions effectively reduce the transmission rate; in others, the growth of the reported number of cases slows down even though policy has little or no effect on the transmission rate. Thus, the effect of policy cannot be uniquely determined based on the reduced form estimates. This result holds regardless of whether time series or panel data is used in reduced form estimation. I also demonstrate that the reduced form estimates of the policy effect based on panel data specifications with two-way fixed effects can have the wrong sign.