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This paper proposes a new way to construct confidence sets for a parameter of interest in models comprised of finitely many 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. The confidence sets are easily computed, and Monte Carlo simulations demonstrate good finite sample performance.