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. In addition, we ...
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. In addition, we advocate the use
of studentization when it is feasible. 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. We present our methodology in the context of
comparing several strategies to a common benchmark and deciding which
strategies actually beat the benchmark. However, our ideas can easily be
extended and/or modied to other contexts, such as making inference for the
individual regression coecients in a multiple regression framework. Some
simulation studies show the improvements of our methods over previous
proposals. We also provide an application to a set of real data.
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