Estimation of nonseparable models with censored dependent variables and endogenous regressors

• Published in: Econometric reviews Vol. 38; no. 1; pp. 4 - 24
• Main Authors: Taylor, Luke, Otsu, Taisuke
• Format: Journal Article
• Language: English
• Published: New York Taylor & Francis 02.01.2019; Taylor & Francis Ltd
• Subjects: Asymptotic properties; Average derivatives; censored dependent variables; Computer simulation; Dependent variables; Econometrics; Economic models; endogeneity; Endogenous; Estimating techniques; Monte Carlo simulation; nonparametric estimation; Nonparametric statistics; nonseparable models; Property; Regression analysis
• ISSN: 0747-4938; 1532-4168
• DOI: 10.1080/07474938.2016.1235310

In this article we develop a nonparametric estimator for the local average response of a censored dependent variable to endogenous regressors in a nonseparable model where the unobservable error term is not restricted to be scalar and where the nonseparable function need not be monotone in the unobservables. We formalize the identification argument put forward in Altonji, Ichimura, and Otsu ( 2012 ), construct a nonparametric estimator, characterize its asymptotic property, and conduct a Monte Carlo investigation to study its small sample properties. Identification is constructive and is achieved through a control function approach. We show that the estimator is consistent and asymptotically normally distributed. The Monte Carlo results are encouraging.