Semiparametric Estimation of a Binary Choice Model with Sample Selection

Arbeitspapier

In this paper we provide semiparametric estimation strategies for a sample selection model with a binary dependent variable. To the best of our knowledge, this has not been done before. We propose a control function approach based on two di erent identifying assumptions. This gives rise to semiparametric estimators which are extensions of the Klein and Spady (1993), maximum score (Manski, 1975) and smoothed maximum score (Horowitz, 1992) estimators. We provide Monte Carlo evidence and an empirical example to study the nite sample properties of our estimators. Finally, we outline an extension of these estimators to the case of endogenous covariates.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Diskussionsbeitrag ; No. 505

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models ; Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities ; Single Equation Models: Single Variables: Instrumental Variables (IV) Estimation
• Subject: Sample selection model ; binary dependent variable ; semiparametric estimation ; control function approach ; endogenous covariates

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Schwiebert, Jörg
• Event: Veröffentlichung
• (who): Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät
• (where): Hannover
• (when): 2012

Further information

• Handle: http://hdl.handle.net/10419/73118
• Last update: 10.03.2025, 11:44 AM CET