Efficiency Bounds for Distribution-free Estimators of the Binary

Stephen R Cosslett

Econometrica, 1987, vol. 55, issue 3, 559-85

Abstract: Lower bounds are derived for the asymptotic variances of regular distribution-free (or semiparametric) estimators of the parameters of the binary-choice model and the censored-regression (Tobit) model. A semiparametric estimator is one that does not require any assumption about the distribution of the stochastic error term in the model, apart from regularity conditions. Comparison of the bounds with the corresponding asymptotic Cramer-Rao bounds for the classical parametric problem shows the loss of information due to lack of a priori knowledge of the functional form of the error distribution. Copyright 1987 by The Econometric Society.

Date: 1987
References: Add references at CitEc
Citations: View citations in EconPapers (38)

Downloads: (external link)
http://links.jstor.org/sici?sici=0012-9682%2819870 ... O%3B2-M&origin=repec full text (application/pdf)
Access to full text is restricted to JSTOR subscribers. See http://www.jstor.org for details.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ecm:emetrp:v:55:y:1987:i:3:p:559-85

Ordering information: This journal article can be ordered from
https://www.economet ... ordering-back-issues

Access Statistics for this article

Econometrica is currently edited by Guido Imbens

More articles in Econometrica from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().