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Comparing Features of Convenient Estimators for Binary Choice Models With Endogenous Regressors

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Author Info

  • Yingying Dong

    (California State University, Irvine)

  • Arthur Lewbel

    ()
    (Boston College)

  • Thomas Tao Yang

    (Boston College)

Abstract

We discuss the relative advantages and disadvantages of four types of convenient estimators of binary choice models when regressors may be endogenous or mismeasured, or when errors are likely to be heteroskedastic. For example, such models arise when treatment is not randomly assigned and outcomes are binary. The estimators we compare are the two stage least squares linear probability model, maximum likelihood estimation, control function estimators, and special regressor methods. We specifically focus on models and associated estimators that are easy to implement. Also, for calculating choice probabilities and regressor marginal effects, we propose the average index function (AIF), which, unlike the average structural function (ASF), is always easy to estimate.

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Bibliographic Info

Paper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 789.

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Date of creation: 15 Feb 2012
Date of revision: 15 May 2012
Publication status: forthcoming, Canadian Journal of Economics
Handle: RePEc:boc:bocoec:789

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Related research

Keywords: Binary choice; Binomial Response; Endogeneity; Measurement Error; Heteroskedasticity; discrete endogenous; censored; random coefficients; Identification; Latent Variable Model.;

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References

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  1. Arthur Lewbel & Susanne M. Schennach, 2003. "A Simple Ordered Data Estimator For Inverse Density Weighted Functions," Boston College Working Papers in Economics 557, Boston College Department of Economics, revised 01 May 2005.
  2. Arthur Lewbel, 2000. "Endogenous Selection Or Treatment Model Estimation," Boston College Working Papers in Economics 462, Boston College Department of Economics, revised 13 Jun 2007.
  3. Thierry Magnac & Eric Maurin, 2004. "Partial Identification in Monotone Binary Models : Discrete Regressors and Interval Data," Working Papers 2004-11, Centre de Recherche en Economie et Statistique.
  4. Lewbel, Arthur, 2000. "Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables," Journal of Econometrics, Elsevier, vol. 97(1), pages 145-177, July.
  5. Jacho-Chávez, David T., 2009. "Efficiency Bounds For Semiparametric Estimation Of Inverse Conditional-Density-Weighted Functions," Econometric Theory, Cambridge University Press, vol. 25(03), pages 847-855, June.
  6. Richard Blundell & James Powell, 2001. "Endogeneity in semiparametric binary response models," CeMMAP working papers CWP05/01, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  7. Shaikh, Azeem M. & Vytlacil, Edward, 2008. "Endogenous binary choice models with median restrictions: A comment," Economics Letters, Elsevier, vol. 98(1), pages 23-28, January.
  8. Yingying Dong & Arthur Lewbel, 2004. "A Simple Estimator for Binary Choice Models with Endogenous Regressors," Boston College Working Papers in Economics 604, Boston College Department of Economics, revised 15 Jun 2012.
  9. Edward Vytlacil & Nese Yildiz, 2007. "Dummy Endogenous Variables in Weakly Separable Models," Econometrica, Econometric Society, vol. 75(3), pages 757-779, 05.
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  11. Blundell, Richard W & Smith, Richard J, 1989. "Estimation in a Class of Simultaneous Equation Limited Dependent Variable Models," Review of Economic Studies, Wiley Blackwell, vol. 56(1), pages 37-57, January.
  12. Klein, R.W. & Spady, R.H., 1991. "An Efficient Semiparametric Estimator for Binary Response Models," Papers 70, Bell Communications - Economic Research Group.
  13. Stefan Hoderlein, 2009. "Endogenous semiparametric binary choice models with heteroscedasticity," CeMMAP working papers CWP34/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  14. Chesher, Andrew, 2009. "Excess heterogeneity, endogeneity and index restrictions," Journal of Econometrics, Elsevier, vol. 152(1), pages 37-45, September.
  15. Hong, Han & Tamer, Elie, 2003. "Endogenous binary choice model with median restrictions," Economics Letters, Elsevier, vol. 80(2), pages 219-225, August.
  16. Andrew Chesher, 2008. "Instrumental variable models for discrete outcomes," CeMMAP working papers CWP30/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  17. 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.
  18. Rivers, Douglas & Vuong, Quang H., 1988. "Limited information estimators and exogeneity tests for simultaneous probit models," Journal of Econometrics, Elsevier, vol. 39(3), pages 347-366, November.
  19. 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.
  20. Joseph G. Altonji & Rosa L. Matzkin, 2005. "Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors," Econometrica, Econometric Society, vol. 73(4), pages 1053-1102, 07.
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Citations

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Cited by:
  1. Olfa KAMMOUN & Mohieddine RAHMOUNI, 2013. "Intellectual Property Rights, Appropriation Instruments and Innovation Activities: Evidence from Tunisian Firms," Cahiers du GREThA 2013-01, Groupe de Recherche en Economie Théorique et Appliquée.
  2. Yingying Dong & Arthur Lewbel, 2012. "A Simple Estimator for Binary Choice Models With Endogenous Regressors," Boston College Working Papers in Economics 807, Boston College Department of Economics.
  3. Andrew Chesher & Adam Rosen, 2013. "What do instrumental variable models deliver with discrete dependent variables?," CeMMAP working papers CWP10/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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