Optimal testing in a class of nonregular models

Abstract

This paper studies optimal hypothesis testing for nonregular econometric models with parameter-dependent support. We consider both one-sided and two-sided hypothesis testing and develop asymptotically uniformly most powerful tests based on a limit experiment. Our two-sided test becomes asymptotically uniformly most powerful without imposing further restrictions such as unbiasedness, and can be inverted to construct a confidence set for the nonregular parameter. Simulation results illustrate desirable finite sample properties of the proposed tests.