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Semiiparametric Selection Models with Binary Outcomes

Author

Listed:
  • Roger Klein

    (Rotgers University)

  • Chan Shen

    (The University of Texas MD Anderson Cancer Center)

  • Francis Vella

    (Georgetown University)

Abstract

This paper addresses the estimation of a semiparametric sample selection index model where both the selection rule and the outcome variable are binary. Since the marginal effects are often of primary interest and are difficult to recover in a semiparametric setting, we develop estimators for both the marginal effects and the underlying model parameters. The marginal effect estimator uses only observations where the selection probability is above a certain threshold. A key innovation is that this high probability set is adaptive to the data. The model parameter estimator is a quasi-likelihood estimator based on regular kernels with bias corrections. We establish their large sample properties and provide simulation evidence confirming that these estimators perform well in finite samples.

Suggested Citation

  • Roger Klein & Chan Shen & Francis Vella, 2014. "Semiiparametric Selection Models with Binary Outcomes," Departmental Working Papers 201403, Rutgers University, Department of Economics.
    • Handle: RePEc:rut:rutres:201403
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    References listed on IDEAS

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    Cited by:

    1. Juan Carlos Escanciano & Lin Zhu, 2013. "Set inferences and sensitivity analysis in semiparametric conditionally identified models," CeMMAP working papers 55/13, Institute for Fiscal Studies.
    2. Juan Carlos Escanciano & Lin Zhu, 2013. "Set inferences and sensitivity analysis in semiparametric conditionally identified models," CeMMAP working papers CWP55/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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    More about this item

    Keywords

    Semiparametric Binary Selection; Marginal Effects;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

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