<p>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.</p>
Authors
![Francis Vella](/sites/default/files/styles/square_desktop/public/2022-07/Frank%20Vella_1.jpg?itok=f9f9VWFz)
Research Associate Georgetown University
Francis Vella is a Research Associate of the IFS and a Professor and the Edmund Villani Chair in Economics at Georgetown University.
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Roger Klein
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Chan Shen
Working Paper details
- DOI
- 10.1920/wp.cem.2011.3011
- Publisher
- IFS
Suggested citation
R, Klein and C, Shen and F, Vella. (2011). Semiparametric selection models with binary outcomes. London: IFS. Available at: https://ifs.org.uk/publications/semiparametric-selection-models-binary-outcomes (accessed: 1 July 2024).
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