期刊名称：HIER Discussion Paper Series / Harvard Institute of Economic Research
出版年度：2002
卷号：2002
出版社：Harvard Institute of Economic Research
摘要：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 e cient point estimators. We show that the maximum likelihood estimator is generally ine cient, but that the Bayes estimator is e cient 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.
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