Asymptotic efficiency in parametric structural models with parameter-dependent support

Keisuke Hirano, Jack R. Porter

Research output: Contribution to journalArticlepeer-review

50 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1307-1338
Number of pages32
Issue number5
StatePublished - 2003
Externally publishedYes


  • Efficiency bounds
  • Limits of experiments
  • Local asymptotic minmax
  • Nonregular models
  • Parameter-dependent support

ASJC Scopus subject areas

  • Economics and Econometrics


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