Premium
Stepwise Multiple Testing as Formalized Data Snooping
Author(s) -
Romano Joseph P.,
Wolf Michael
Publication year - 2005
Publication title -
econometrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.7
H-Index - 199
eISSN - 1468-0262
pISSN - 0012-9682
DOI - 10.1111/j.1468-0262.2005.00615.x
Subject(s) - multiple comparisons problem , benchmark (surveying) , computer science , context (archaeology) , statistical hypothesis testing , set (abstract data type) , econometrics , statistics , data mining , machine learning , mathematics , paleontology , geodesy , biology , programming language , geography
In econometric applications, often several hypothesis tests are carried out at once. The problem then becomes how to decide which hypotheses to reject, accounting for the multitude of tests. This paper suggests a stepwise multiple testing procedure that asymptotically controls the familywise error rate. Compared to related single‐step methods, the procedure is more powerful and often will reject more false hypotheses. In addition, we advocate the use of studentization when feasible. Unlike some stepwise methods, the method implicitly captures the joint dependence structure of the test statistics, which results in increased ability to detect false hypotheses. The methodology is presented in the context of comparing several strategies to a common benchmark. However, our ideas can easily be extended to other contexts where multiple tests occur. Some simulation studies show the improvements of our methods over previous proposals. We also provide an application to a set of real data.