Estimation of marginal effects in semiparametric selection models with binary outcomes

Roger Klein, Chan Shen, Francis Vella

Research output: Contribution to journalArticle

4 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)82-94
Number of pages13
JournalJournal of Econometrics
Volume185
Issue number1
DOIs
StatePublished - Jan 1 2015

Fingerprint

Binary Outcomes
Selection Model
Semiparametric Model
Estimator
Innovation
Sample Selection
Selection Rules
Monte Carlo Study
Selection model
Marginal effects
Model Selection
Binary

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics
  • Applied Mathematics
  • History and Philosophy of Science

Cite this

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

In: Journal of Econometrics, Vol. 185, No. 1, 01.01.2015, p. 82-94.

Research output: Contribution to journalArticle

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