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Semiparametric estimation of binary response models with endogenous regressors

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  • Rothe, Christoph

Abstract

In this paper, we propose a two-step semiparametric maximum likelihood (SML) estimator for the coefficients of a single index binary choice model with endogenous regressors when identification is achieved via a control function approach. The first step consists of estimating a reduced form equation for the endogenous regressors and extracting the corresponding residuals. In the second step, the latter are added as control variates to the outcome equation, which is in turn estimated by SML. We establish the estimator's -consistency and asymptotic normality. In a simulation study, we compare the properties of our estimator with those of existing alternatives, highlighting the advantages of our approach.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 153 (2009)
Issue (Month): 1 (November)
Pages: 51-64

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Handle: RePEc:eee:econom:v:153:y:2009:i:1:p:51-64

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: Binary choice model Semiparametric maximum likelihood Endogenous regressors Instrumental variables Control function;

References

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Citations

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Cited by:
  1. Escanciano, Juan Carlos & Jacho-Chávez, David T. & Lewbel, Arthur, 2014. "Uniform convergence of weighted sums of non and semiparametric residuals for estimation and testing," Journal of Econometrics, Elsevier, vol. 178(P3), pages 426-443.
  2. Mammen, Enno & Rothe, Christoph & Schienle, Melanie, 2011. "Semiparametric Estimation with Generated Covariates," IZA Discussion Papers 6084, Institute for the Study of Labor (IZA).
  3. Schwiebert, Jörg, 2012. "Semiparametric Estimation of a Binary Choice Model with Sample Selection," Hannover Economic Papers (HEP) dp-505, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  4. Xibin Zhang & Maxwell L. King & Han Lin Shang, 2013. "A sampling algorithm for bandwidth estimation in a nonparametric regression model with a flexible error density," Monash Econometrics and Business Statistics Working Papers 20/13, Monash University, Department of Econometrics and Business Statistics.
  5. Zhang, Zhengyu & He, Xiaobo, 2012. "Estimation of a heteroscedastic binary choice model with an endogenous dummy regressor," Economics Letters, Elsevier, vol. 117(3), pages 753-757.
  6. Xibin Zhang & Maxwell L. King & Han Lin Shang, 2011. "Bayesian estimation of bandwidths for a nonparametric regression model with a flexible error density," Monash Econometrics and Business Statistics Working Papers 10/11, Monash University, Department of Econometrics and Business Statistics.
  7. Xiaofeng Lv & Rui Li, 2013. "Smoothed empirical likelihood analysis of partially linear quantile regression models with missing response variables," AStA Advances in Statistical Analysis, Springer, vol. 97(4), pages 317-347, October.
  8. Halkos, George, 2012. "The use of contingent valuation in assessing marine and coastal ecosystems’ water quality: A review," MPRA Paper 42183, University Library of Munich, Germany.
  9. Schwiebert, Jörg, 2012. "Semiparametric Estimation of a Sample Selection Model in the Presence of Endogeneity," Hannover Economic Papers (HEP) dp-504, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  10. Jerome M. Krief, . "Kernel Weighted Smoothed Maximum Score Estimation for Applied Work," Departmental Working Papers 2011-07, Department of Economics, Louisiana State University.

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