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

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  • Zhewen Pan
  • Xianbo Zhou
  • Yahong Zhou

Abstract

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.

Suggested Citation

  • Zhewen Pan & Xianbo Zhou & Yahong Zhou, 2022. "Semiparametric Estimation of a Censored Regression Model Subject to Nonparametric Sample Selection," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 141-151, January.
    • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:1:p:141-151
    DOI: 10.1080/07350015.2020.1784746
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    Cited by:

    1. Zhewen Pan & Zhengxin Wang & Junsen Zhang & Yahong Zhou, 2024. "Marginal treatment effects in the absence of instrumental variables," Papers 2401.17595, arXiv.org.

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