Semiparametric Estimation of a Censored Regression Model Subject to Nonparametric Sample Selection

This study proposes a semiparametric estimation method for a censored regression model subject to nonparametric sample selection without the exclusion restriction. Consistency and asymptotic normality of the proposed estimator are established under mild regularity conditions. A Monte Carlo simulatio...

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Bibliographic Details
Published inJournal of business & economic statistics Vol. 40; no. 1; pp. 141 - 151
Main Authors Pan, Zhewen, Zhou, Xianbo, Zhou, Yahong
Format Journal Article
LanguageEnglish
Published Alexandria Taylor & Francis 2022
Taylor & Francis Ltd
Subjects
Asymptotic normality
Female smoking
Integrated nonlinear least squares
Limited dependent variable
Nonparametric statistics
Survival function
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Summary:This study proposes a semiparametric estimation method for a censored regression model subject to nonparametric sample selection without the exclusion restriction. Consistency and asymptotic normality of the proposed estimator are established under mild regularity conditions. A Monte Carlo simulation study indicates that the estimator performs well in various designs and outperforms parametric maximum likelihood estimators. An empirical application to female smoking is provided to illustrate the usefulness of the estimator.
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SourceType-Scholarly Journals-1
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ISSN:0735-0015
1537-2707
DOI:10.1080/07350015.2020.1784746