Semiparametric Selection Models with Binary Outcomes

Roger Klein (), Chan Shen () and Francis Vella
Additional contact information
Chan Shen: University of Texas MD Anderson Cancer Center

No 6008, IZA Discussion Papers from Institute of Labor Economics (IZA)

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 only uses observations which are members of a high probability set in which the selection problem is not present. A key innovation is that this high probability set is data dependent. 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: sample selection; binary outcomes; marginal effects; semiparametric (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Pages: 45 pages
Date: 2011-10
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Semiiparametric Selection Models with Binary Outcomes (2014)
Working Paper: Semiparametric selection models with binary outcomes (2011)
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