Semiparametric estimation of a censored regression model with an unknown transformation of the dependent variable

被引：28

作者：

Gorgens, T

Horowitz, JL ^{[1
]}

机构：

[1] Univ Iowa, Dept Econ, Iowa City, IA 52242 USA

[2] Univ New S Wales, Sch Econ, Sydney, NSW 2052, Australia

来源：

JOURNAL OF ECONOMETRICS | 1999年 / 90卷 / 02期

基金：

美国国家科学基金会;

关键词：

semiparametric estimation; transformation model; empirical process; Kaplan-Meier estimator; proportional hazards model;

D O I：

10.1016/S0304-4076(98)00040-2

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This paper presents a method for estimating the model A(Y)= min(beta'x + U, C), where Y is a scalar, ii is an unknown increasing function, X is a vector of explanatory variables, a is a vector of unknown parameters, U has unknown cumulative distribution function F, and C is a censoring threshold. It is not assumed that A and F belong to known parametric families; they are estimated nonparametrically. This model includes many widely used models as special cases, including the proportional hazards model with unobserved heterogeneity. The paper develops n(1/2)-consistent, asymptotically normal estimators of A and F. Estimators of beta that are n(1/2)-consistent and asymptotically normal already exist. The results of Monte Carlo experiments illustrate the finite-sample behavior of the estimators. (C) 1999 Elsevier Science S.A. All rights reserved. JEL classification: C14; C24; C41.

引用

页码：155 / 191

页数：37

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