Estimating Derivatives in Nonseparable Models With Limited Dependent Variables

• 成果类型: Article
• 署名作者: Altonji, Joseph G.; Ichimura, Hidehiko; Otsu, Taisuke
• 署名单位: Yale University; University of Tokyo; Yale University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA8004
• 发表日期: 2012
• 页码: 1701-1719
• 关键词: Nonparametric identification

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.