Asymptotic efficiency in parametric structural models with parameter-dependent support

Keisuke Hirano, Jack R. Porter

Research output: Contribution to journalArticle

34 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1307-1338
Number of pages32
JournalEconometrica
Volume71
Issue number5
DOIs
StatePublished - Jan 1 2003

Fingerprint

Structural model
Estimator
Asymptotic efficiency
Asymptotic distribution
Intuition
Maximum likelihood estimator
Auctions
Experiment
Limiting distribution
Loss function

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

Cite this

@article{e3f8c0f9b1f8458da22a2199a80d5c34,
title = "Asymptotic efficiency in parametric structural models with parameter-dependent support",
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.",
author = "Keisuke Hirano and Porter, {Jack R.}",
year = "2003",
month = "1",
day = "1",
doi = "10.1111/1468-0262.00451",
language = "English (US)",
volume = "71",
pages = "1307--1338",
journal = "Econometrica",
issn = "0012-9682",
publisher = "Wiley-Blackwell",
number = "5",

}

Asymptotic efficiency in parametric structural models with parameter-dependent support. / Hirano, Keisuke; Porter, Jack R.

In: Econometrica, Vol. 71, No. 5, 01.01.2003, p. 1307-1338.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Asymptotic efficiency in parametric structural models with parameter-dependent support

AU - Hirano, Keisuke

AU - Porter, Jack R.

PY - 2003/1/1

Y1 - 2003/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0142007875&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0142007875&partnerID=8YFLogxK

U2 - 10.1111/1468-0262.00451

DO - 10.1111/1468-0262.00451

M3 - Article

VL - 71

SP - 1307

EP - 1338

JO - Econometrica

JF - Econometrica

SN - 0012-9682

IS - 5

ER -