  This paper derives asymptotic approximations to the power of Cramer-von Mises
(CvM) style tests for inference on a finite dimensional parameter defined by
conditional moment inequalities in the case where the parameter is set
identified. Combined with power results for Kolmogorov-Smirnov (KS) tests,
these results can be used to choose the optimal test statistic, weighting
function and, for tests based on kernel estimates, kernel bandwidth. The
results show that, in the setting considered here, KS tests are preferred to
CvM tests, and that a truncated variance weighting is preferred to bounded
weightings.
