Inference with Few Heterogeneous Clusters

• 成果类型: Article
• 署名作者: Ibragimov, Rustam; Mueller, Ulrich K.
• 署名单位: Imperial College London; Princeton University
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00545
• 发表日期: 2016-03
• 页码: 83-96
• 关键词: panel-data; Robust Inference; in-differences; time-series; t-test; heteroskedasticity; tests; errors; bounds; variables

摘要

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 show how to 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.