Asymptotic Efficiency in Parametric Structural Models with Parameter-Dependent Support

Keisuke Hirano and Jack R. Porter

No 1988, Harvard Institute of Economic Research Working Papers from Harvard - Institute of Economic Research

Abstract: In certain auction, search, and related models, the boundary of the support of the observed data depends on some of the parameters of interest. For such nonregular models, standard asymptotic distribution theory does not apply. Previous work has focused on characterizing the nonstandard limiting distributions of particular estimators in these models. In contrast, we study the problem of constructing efficient point estimators. We show that the maximum likelihood estimator is generally inefficient, but that the Bayes estimator is efficient according to the local asymptotic minmax criterion for conventional loss functions. We provide intuition for this result using Le Cam's limits of experiments framework.

Date: 2002
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Journal Article: Asymptotic Efficiency in Parametric Structural Models with Parameter-Dependent Support (2003)
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