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We propose a simple test for moment inequalities that has exact size innormal models with known variance and has uniformly asymptotically exact sizemore generally. The test compares the quasi-likelihood ratio statistic to achi-squared critical value, where the degree of freedom is the rank of theinequalities that are active in finite samples. The test requires no simulationand thus is computationally fast and especially suitable for constructingconfidence sets for parameters by test inversion. It uses no tuning parameterfor moment selection and yet still adapts to the slackness of the momentinequalities. Furthermore, we show how the test can be easily adapted forinference on subvectors for the common empirical setting of conditional momentinequalities with nuisance parameters entering linearly.

Simple Adaptive Size-Exact Testing for Full-Vector and Subvector Inference in Moment Inequality Models