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Semiparametric Estimation of a Binary Choice Model with Sample Selection

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  • Schwiebert, Jörg
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    Abstract

    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.

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    File URL: http://diskussionspapiere.wiwi.uni-hannover.de/pdf_bib/dp-505.pdf
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    Bibliographic Info

    Paper provided by Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät in its series Hannover Economic Papers (HEP) with number dp-505.

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    Length: 35 pages
    Date of creation: Oct 2012
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    Handle: RePEc:han:dpaper:dp-505

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    Related research

    Keywords: Sample selection model; binary dependent variable; semiparametric estimation; control function approach; endogenous covariates;

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    References

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    1. Xiaohong Chen & Oliver Linton & Ingrid Van Keilegom, 2003. "Estimation of Semiparametric Models when the Criterion Function is not Smooth," STICERD - Econometrics Paper Series /2003/450, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    2. Whitney Newey, 1999. "Two Step Series Estimation of Sample Selection Models," Working papers 99-04, Massachusetts Institute of Technology (MIT), Department of Economics.
    3. Manski, Charles F. & Thompson, T. Scott, 1986. "Operational characteristics of maximum score estimation," Journal of Econometrics, Elsevier, vol. 32(1), pages 85-108, June.
    4. Van de Ven, Wynand P. M. M. & Van Praag, Bernard M. S., 1981. "The demand for deductibles in private health insurance : A probit model with sample selection," Journal of Econometrics, Elsevier, vol. 17(2), pages 229-252, November.
    5. Boyes, William J. & Hoffman, Dennis L. & Low, Stuart A., 1989. "An econometric analysis of the bank credit scoring problem," Journal of Econometrics, Elsevier, vol. 40(1), pages 3-14, January.
    6. Meng, Chun-Lo & Schmidt, Peter, 1985. "On the Cost of Partial Observability in the Bivariate Probit Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 26(1), pages 71-85, February.
    7. Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
    8. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-31, May.
    9. Rothe, Christoph, 2009. "Semiparametric estimation of binary response models with endogenous regressors," Journal of Econometrics, Elsevier, vol. 153(1), pages 51-64, November.
    10. Ekaterini Kyriazidou, 1997. "Estimation of a Panel Data Sample Selection Model," Econometrica, Econometric Society, vol. 65(6), pages 1335-1364, November.
    11. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
    12. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
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