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FORMALIZED DATA SNOOPING BASED ON GENERALIZED ERROR RATES

Published online by Cambridge University Press:  30 November 2007

Joseph P. Romano ,
Azeem M. Shaikh  and
Michael Wolf
Joseph P. Romano
Affiliation:
Stanford University
Azeem M. Shaikh
Affiliation:
University of Chicago
Michael Wolf
Affiliation:
University of Zurich
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Abstract

It is common in econometric applications that several hypothesis tests are carried out simultaneously. The problem then becomes how to decide which hypotheses to reject, accounting for the multitude of tests. The classical approach is to control the familywise error rate (FWE), which is the probability of one or more false rejections. But when the number of hypotheses under consideration is large, control of the FWE can become too demanding. As a result, the number of false hypotheses rejected may be small or even zero. This suggests replacing control of the FWE by a more liberal measure. To this end, we review a number of recent proposals from the statistical literature. We briefly discuss how these procedures apply to the general problem of model selection. A simulation study and two empirical applications illustrate the methods.We thank three anonymous referees for helpful comments that have led to an improved presentation of the paper. The research of the third author has been partially supported by the Spanish Ministry of Science and Technology and FEDER, Grant BMF2003-03324.

Type
Research Article
Information
Econometric Theory , Volume 24 , Issue 2 , April 2008 , pp. 404 - 447
Copyright
© 2008 Cambridge University Press

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