A robust permutation test for subvector inference in linear regressions

• 成果类型: Article
• 署名作者: D'Haultfoeuille, Xavier; Tuvaandorj, Purevdorj
• 署名单位: Institut Polytechnique de Paris; ENSAE Paris; York University - Canada
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE2269
• 发表日期: 2024
• 页码: 27-87
• 关键词: Linear regressions; permutation tests; exact tests; asymptotic validity; heteroskedasticity; C12; C15; C21

摘要

We develop a new permutation test for inference on a subvector of coefficients in linear models. The test is exact when the regressors and the error terms are independent. Then we show that the test is asymptotically of correct level, consistent, and has power against local alternatives when the independence condition is relaxed, under two main conditions. The first is a slight reinforcement of the usual absence of correlation between the regressors and the error term. The second is that the number of strata, defined by values of the regressors not involved in the subvector test, is small compared to the sample size. The latter implies that the vector of nuisance regressors is discrete. Simulations and empirical illustrations suggest that the test has good power in practice if, indeed, the number of strata is small compared to the sample size.