  Much empirical research in economics and finance involves simultaneously
testing multiple hypotheses. This paper proposes extended MinP (EMinP) tests by
expanding the minimand set of the MinP test statistic to include the $p$%
-value of a global test such as a likelihood ratio test. We show that, compared
with MinP tests, EMinP tests may considerably improve the global power in
rejecting the intersection of all individual hypotheses. Compared with closed
tests EMinP tests have the computational advantage by sharing the benefit of
the stepdown procedure of MinP tests and can have a better global power over
the tests used to construct closed tests. Furthermore, we argue that EMinP
tests may be viewed as a tool to prevent data snooping when two competing tests
that have distinct global powers are exploited. Finally, the proposed tests are
applied to an empirical application on testing the effects of exercise.
