Inference with Few Heterogeneous Clusters
研究了在少量异质性集群下，如何用两样本t统计量比较处理组和对照组的参数，并开发了一个检验合适聚类水平的统计方法，适用于聚类、时间序列和空间相关数据。
Suppose estimating a model on each of a small number of potentially heterogeneous clusters yields approximately independent, unbiased, and Gaussian parameter estimators. We make two contributions in this setup. First, we showhowto compare a scalar parameter of interest between treatment and control units using a two-sample t-statistic, extending previous results for the one-sample t-statistic. Second, we develop a test for the appropriate level of clustering; it tests the null hypothesis that clustered standard errors from a much finer partition are correct. We illustrate the approach by revisiting empirical studies involving clustered, time series, and spatially correlated data.