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Asymptotic Efficiency in Parametric Structural Models with Parameter-Dependent Support

  • Keisuke Hirano
  • Jack R. Porter

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 ecient point estimators. We show that the maximum likelihood estimator is generally inecient, but that the Bayes estimator is ecient 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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Paper provided by Harvard - Institute of Economic Research in its series Harvard Institute of Economic Research Working Papers with number 1988.

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Date of creation: 2002
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Handle: RePEc:fth:harver:1988
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