Department of Economics and Business Economics

Luke Nicholas Taylor

Estimation of Nonseparable Models with Censored Dependent Variables and Endogenous Regressors

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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.

Original languageEnglish
JournalEconometric Reviews
Pages (from-to)4-24
Number of pages21
Publication statusPublished - 2019
Externally publishedYes

    Research areas

  • Average derivatives, censored dependent variables, endogeneity, nonparametric estimation, nonseparable models, NONPARAMETRIC IDENTIFICATION, TRANSFERS

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