Estimation of nonseparable models with censored dependent variables and endogenous regressors

Luke Taylor and Taisuke Otsu

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: 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 Altonji, J. G., Ichimura, H., Otsu, T. (2012). Estimating derivatives in nonseparable models with limited dependent variables. Econometrica 80:1701–1719. [CrossRef], [Web of Science ®] ), 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.

JEL-codes: J1 (search for similar items in EconPapers)
Date: 2016-09-19
New Economics Papers: this item is included in nep-ore
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Published in Econometric Reviews, 19, September, 2016, pp. 1-21. ISSN: 0747-4938

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Related works:
Journal Article: Estimation of nonseparable models with censored dependent variables and endogenous regressors (2019)
Working Paper: Estimation of Nonseparable Models with Censored Dependent Variables and Endogenous Regressors (2014)
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