Permutation Inference with a Finite Number of Heterogeneous Clusters

• 成果类型: Article
• 署名作者: Hagemann, Andreas
• 署名单位: University of Michigan System; University of Michigan
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_01300
• 发表日期: 2025-07
• 页码: 1115-1122
• 关键词: in-differences; difference; bootstrap; tests

摘要

I introduce a simple permutation procedure to test conventional (nonsharp) hypotheses about the effect of a binary treatment in the presence of a finite number of large, heterogeneous clusters when the treatment effect is identified by comparisons across clusters. The procedure asymptotically controls size by applying a level-adjusted permutation test to a suitable statistic. The adjusted permutation test is easy to implement in practice and performs well at conventional levels of significance with at least four treated clusters and a similar number of control clusters. It is particularly robust to situations where some clusters are much more variable than others.