### 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 focus on developing an estimator for the marginal effects. This 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. We establish the large sample properties of the marginal effect estimator as well as those for an index estimator upon which it depends. Monte Carlo studies show that these estimators perform well in finite samples.

Original language | English (US) |
---|---|

Pages (from-to) | 82-94 |

Number of pages | 13 |

Journal | Journal of Econometrics |

Volume | 185 |

Issue number | 1 |

DOIs | |

State | Published - Jan 1 2015 |

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### All Science Journal Classification (ASJC) codes

- Economics and Econometrics

### Cite this

*Journal of Econometrics*,

*185*(1), 82-94. https://doi.org/10.1016/j.jeconom.2014.10.006

}

*Journal of Econometrics*, vol. 185, no. 1, pp. 82-94. https://doi.org/10.1016/j.jeconom.2014.10.006

**Estimation of marginal effects in semiparametric selection models with binary outcomes.** / Klein, Roger; Shen, Chan; Vella, Francis.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Estimation of marginal effects in semiparametric selection models with binary outcomes

AU - Klein, Roger

AU - Shen, Chan

AU - Vella, Francis

PY - 2015/1/1

Y1 - 2015/1/1

N2 - 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 focus on developing an estimator for the marginal effects. This 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. We establish the large sample properties of the marginal effect estimator as well as those for an index estimator upon which it depends. Monte Carlo studies show that these estimators perform well in finite samples.

AB - 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 focus on developing an estimator for the marginal effects. This 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. We establish the large sample properties of the marginal effect estimator as well as those for an index estimator upon which it depends. Monte Carlo studies show that these estimators perform well in finite samples.

UR - http://www.scopus.com/inward/record.url?scp=84922550677&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84922550677&partnerID=8YFLogxK

U2 - 10.1016/j.jeconom.2014.10.006

DO - 10.1016/j.jeconom.2014.10.006

M3 - Article

AN - SCOPUS:84922550677

VL - 185

SP - 82

EP - 94

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 1

ER -