Estimating Derivatives in Nonseparable Models With Limited Dependent Variables

被引：9

作者：

Altonji, Joseph G. ^{[1
]}

Ichimura, Hidehiko ^{[2
]}

Otsu, Taisuke ^{[1
,3
]}

机构：

[1] Yale Univ, Dept Econ, New Haven, CT 06520 USA

[2] Univ Tokyo, Grad Sch Econ, Bunkyo Ku, Tokyo 1130033, Japan

[3] Yale Univ, Cowles Fdn, New Haven, CT 06520 USA

来源：

ECONOMETRICA | 2012年 / 80卷 / 04期

基金：

美国国家科学基金会;

关键词：

Nonseparable models; nonparametric; semiparametric; extreme quantiles; censored dependent variables; average derivatives; NONPARAMETRIC IDENTIFICATION;

D O I：

10.3982/ECTA8004

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

We present a simple way to estimate the effects of changes in a vector of observable variables X on a limited dependent variable Y when Y is a general nonseparable function of X and unobservables, and X is independent of the unobservables. We treat models in which Y is censored from above, below, or both. The basic idea is to first estimate the derivative of the conditional mean of Y given X at x with respect to x on the uncensored sample without correcting for the effect of x on the censored population. We then correct the derivative for the effects of the selection bias. We discuss nonparametric and semiparametric estimators for the derivative. We also discuss the cases of discrete regressors and of endogenous regressors in both cross section and panel data contexts.

引用

页码：1701 / 1719

页数：19

共 34 条

[1]

Altonji J. G., 2008, 1668 COWL FDN

[2]

Altonji J. G., 2012, ECONOMETRICA SUPPLEM, V80

[3] Cross section and panel data estimators for nonseparable models with endogenous regressors [J].