Semiiparametric Selection Models with Binary Outcomes

Roger Klein (rogerwklein@gmail.com), Chan Shen (cshen@mdanderson.org) and Francis Vella
Additional contact information
Roger Klein: Rotgers University
Chan Shen: The University of Texas MD Anderson Cancer Center

Departmental Working Papers from Rutgers University, Department of Economics

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.

Keywords: Semiparametric Binary Selection; Marginal Effects (search for similar items in EconPapers)
JEL-codes: C1 C3 C4 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2014-05-22
New Economics Papers: this item is included in nep-ore
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http://www.sas.rutgers.edu/virtual/snde/wp/2014-03.pdf (application/pdf)

Related works:
Working Paper: Semiparametric selection models with binary outcomes (2011)
Working Paper: Semiparametric Selection Models with Binary Outcomes (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:rut:rutres:201403

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