Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/64700
Authors: 
Klein, Roger
Shen, Chan
Vella, Francis
Year of Publication: 
2011
Series/Report no.: 
cemmap working paper CWP30/11
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 confirminng that these estimators perform well in finite samples.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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