  We propose a hypothesis test that allows for many tested restrictions in a
heteroskedastic linear regression model. The test compares the conventional F
statistic to a critical value that corrects for many restrictions and
conditional heteroskedasticity. This correction uses leave-one-out estimation
to correctly center the critical value and leave-three-out estimation to
appropriately scale it. The large sample properties of the test are established
in an asymptotic framework where the number of tested restrictions may be fixed
or may grow with the sample size, and can even be proportional to the number of
observations. We show that the test is asymptotically valid and has non-trivial
asymptotic power against the same local alternatives as the exact F test when
the latter is valid. Simulations corroborate these theoretical findings and
suggest excellent size control in moderately small samples, even under strong
heteroskedasticity.
