Robust and Efficient Inference for Non-Regular Semiparametric Models


This paper considers hypothesis testing problems in semiparametric models which may be non-regular for certain values of a potentially infinite dimensional nuisance parameter. I establish that, under mild regularity conditions, tests based on the efficient score function provide locally uniform size control and enjoy maximin optimality properties. This approach is applicable to situations with (i) identification failures, (ii) boundary problems and (iii) distortions induced by the use of regularised estimators. Full details are worked out for two examples: a single index model where the link function may be relatively flat and a linear simultaneous equations model that is (weakly) identified by non-Gaussian errors. In practice the tests are easy to implement and rely on χ2 critical values.

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