FORMALIZED DATA SNOOPING BASED ON GENERALIZED ERROR RATES

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+