The Romano–Wolf multiple-hypothesis correction in Stata

Damian Clarke, Joseph P. Romano () and Michael Wolf
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Joseph P. Romano: Stanford University

Stata Journal, 2020, vol. 20, issue 4, 812-843

Abstract: When considering multiple-hypothesis tests simultaneously, standard statistical techniques will lead to overrejection of null hypotheses unless the multi- plicity of the testing framework is explicitly considered. In this article, we discuss the Romano–Wolf multiple-hypothesis correction and document its implementa- tion in Stata. The Romano–Wolf correction (asymptotically) controls the fami- lywise error rate, that is, the probability of rejecting at least one true null hy- pothesis among a family of hypotheses under test. This correction is considerably more powerful than earlier multiple-testing procedures, such as the Bonferroni and Holm corrections, given that it takes into account the dependence structure of the test statistics by resampling from the original data. We describe a command, rwolf, that implements this correction and provide several examples based on a wide range of models. We document and discuss the performance gains from us- ing rwolf over other multiple-testing procedures that control the familywise error rate.

Keywords: rwolf; bootstrap; familywise error rate; multiple-hypothesis testing; permutation methods; rwolf; stepdown procedure (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (111)

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Working Paper: The Romano-Wolf Multiple Hypothesis Correction in Stata (2019)
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DOI: 10.1177/1536867X20976314

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