A Practitioner’s Guide to Cluster-Robust Inference
针对数据分组为聚类时的回归统计推断问题，指出默认标准误可能高估估计精度，并介绍了基于聚类稳健标准误的方法及实践中可能出现的复杂情况。
Abstract We consider statistical inference for regression when data are grouped into clusters, with regression model errors independent across clusters but correlated within clusters. Examples include data on individuals with clustering on village or region or other category such as industry, and state-year differences-in-differences studies with clustering on state. In such settings, default standard errors can greatly overstate estimator precision. Instead, if the number of clusters is large, statistical inference after OLS should be based on cluster-robust standard errors. We outline the basic method as well as many complications that can arise in practice. These include cluster-specific fixed effects, few clusters, multiway clustering, and estimators other than OLS.