 Multiple testing with covariate adjustment in experimental economicsJohn List, Azeem Shaikh and Atom Vayalinkal () Journal of Applied Econometrics, 2023, vol. 38, issue 6, 920-939 Abstract: This paper provides a framework for testing multiple null hypotheses simultaneously using experimental data in which simple random sampling is used to assign treatment status to units. Using general results from the multiple testing literature, we develop under weak assumptions a procedure that (i) asymptotically controls the familywise error rate—the probability of one or more false rejections—and (ii) is asymptotically balanced in that the marginal probability of rejecting any true null hypothesis is approximately equal in large samples. Our procedure improves upon classical methods by incorporating information about the joint dependence structure of the test statistics when determining which null hypotheses to reject, leading to gains in power. An important point of departure from prior work is that we exploit observed, baseline covariates to obtain further gains in power. The precise way in which we incorporate these covariates is based on recent results from the statistics literature in order to ensure that inferences are typically more powerful in large samples. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wly:japmet:v:38:y:2023:i:6:p:920-939 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of Applied Econometrics is currently edited by M. Hashem Pesaran More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd. |
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