Abstract

http://ssrn.com/abstract=563209
 
 

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Stepwise Multiple Testing as Formalized Data Snooping


Michael Wolf


University of Zurich - Department of Economics

Joseph P. Romano


Stanford University - Department of Statistics

October 2003

UPF Working Paper No. 712

Abstract:     
It is common in econometric applications that several hypothesis tests are carried out at the same time. The problem then becomes how to decide which hypotheses to reject, accounting for the multitude of tests. In this paper, we suggest a stepwise multiple testing procedure which asymptotically controls the familywise error rate at a desired level. Compared to related single-step methods, our procedure is more powerful in the sense that it often will reject more false hypotheses.

Unlike some stepwise methods, our method implicitly captures the joint dependence structure of the test statistics, which results in increased ability to detect alternative hypotheses. We prove our method asymptotically controls the familywise error rate under minimal assumptions. Some simulation studies show the improvements of our methods over previous proposals. We also provide an application to a set of real data.

Number of Pages in PDF File: 36

Keywords: Bootstrap, data snooping, familywise error, multiple testing, step-down method

JEL Classification: C12, C14, C52

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Date posted: July 11, 2004  

Suggested Citation

Wolf, Michael and Romano, Joseph P., Stepwise Multiple Testing as Formalized Data Snooping (October 2003). UPF Working Paper No. 712. Available at SSRN: http://ssrn.com/abstract=563209 or http://dx.doi.org/10.2139/ssrn.563209

Contact Information

Michael Wolf (Contact Author)
University of Zurich - Department of Economics ( email )
Wilfriedstrasse 6
Zurich, 8032
Switzerland
Joseph P. Romano
Stanford University - Department of Statistics ( email )
Stanford, CA 94305
United States
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