Asymptotic Behavior of a t-Test Robust to Cluster Heterogeneity

• 成果类型: Article
• 署名作者: Carter, Andrew V.; Schnepel, Kevin T.; Steigerwald, Douglas G.
• 署名单位: University of California System; University of California Santa Barbara; University of Sydney
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00639
• 发表日期: 2017-10
• 页码: 698-709
• 关键词: inference

摘要

For a cluster-robust t-statistic under cluster heterogeneity we establish that the cluster-robust t-statistic has a gaussian asymptotic null distribution and develop the effective number of clusters, which scales down the actual number of clusters, as a guide to the behavior of the test statistic. The implications for hypothesis testing in applied work are that the number of clusters, rather than the number of observations, should be reported as the sample size, and the effective number of clusters should be reported to guide inference. If the effective number of clusters is large, testing based on critical values from a normal distribution is appropriate.