Confidence sets for partially identified parameters that satisfy a finite number of moment inequalities

This paper proposes a computationally simple way to construct confidence sets for a parameter of interest in models comprised of moment inequalities. Building on results from the literature on multivariate one-sided tests, I show how to test the hypothesis that any particular parameter value is logically consistent with the maintained moment inequalities. The associated test statistic has an asymptotic chi-bar-square distribution, and can be inverted to construct an asymptotic confidence set for the parameter of interest, even if that parameter is only partially identified. Critical values for the test are easily computed, and a Monte Carlo study demonstrates implementation and finite sample performance.