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Multiple testing with covariate adjustment in experimental economics

Multiple testing with covariate adjustment in experimental economics 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Econometrics Wiley

Multiple testing with covariate adjustment in experimental economics

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References (42)

Publisher
Wiley
Copyright
© 2023 John Wiley & Sons, Ltd.
ISSN
0883-7252
eISSN
1099-1255
DOI
10.1002/jae.2985
Publisher site
See Article on Publisher Site

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.

Journal

Journal of Applied EconometricsWiley

Published: Sep 1, 2023

Keywords: experiments; familywise error rate; multiple testing; treatment effects

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