Asymptotic Efficiency in Parametric Structural Models with Parameter-Dependent Support
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 of creation:||2002|
|Date of revision:|
|Contact details of provider:|| Postal: 200 Littauer Center, Cambridge, MA 02138|
Web page: http://www.economics.harvard.edu/journals/hier
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:fth:harver:1988. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Thomas Krichel)
If references are entirely missing, you can add them using this form.