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A Simple Estimator for Binary Choice Models with Endogenous Regressors

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  • Yingying Dong

    (California State University, Fullerton)

  • Arthur Lewbel

    ()
    (Boston College)

Abstract

This paper provides a few variants of a simple estimator for binary choice models with endogenous or mismeasured regressors, or with heteroskedastic errors. Unlike control function methods, which are generally only valid when endogenous regressors are continuous, the estimators proposed here can be used with limited, censored, continuous, or discrete endogenous regressors, and they allow for latent errors having heteroskedasticity of unknown form, including random coefficients. The variants of special regressor based estimators we provide are numerically trivial to implement. We illustrate these methods with an empirical application estimating migration probabilities within the US.

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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 604.

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Date of creation: 31 Aug 2004
Date of revision: 15 Jun 2012
Handle: RePEc:boc:bocoec:604

Note: Previously circulated as "Simple Estimators for Binary Choice Models with Endogenous Regressors"
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Keywords: Binary choice; Binomial Response; Endogeneity; Measurement Error; Heteroskedasticity; discrete endogenous; censored; random coefficients; Identification; Latent Variable Model.;

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Citations

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Cited by:
  1. Bhattacharjee, A. & Holly, S., 2010. "Understanding Interactions in Social Networks and Committees," Cambridge Working Papers in Economics 1003, Faculty of Economics, University of Cambridge.
  2. Kamhon Kan & Chihwa Kao, 2005. "Simulation-Based Two-Step Estimation with Endogenous Regressors," Center for Policy Research Working Papers 76, Center for Policy Research, Maxwell School, Syracuse University.
  3. Lööf, Hans, 2007. "Multinational Firms and Innovation: The Role of R&D Collaboration, Markets and Ownership," Working Paper Series in Economics and Institutions of Innovation 90, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
  4. Yingying Dong & Arthur Lewbel & Thomas Tao Yang, 2012. "Comparing Features of Convenient Estimators for Binary Choice Models With Endogenous Regressors," Boston College Working Papers in Economics 789, Boston College Department of Economics, revised 15 May 2012.
  5. Luigi Benfratello & Fabio Schiantarelli & Alessandro Sembenelli, 2005. "Banks and Innovation: Microeconometric Evidence on Italian Firms," Boston College Working Papers in Economics 631, Boston College Department of Economics, revised 13 Jun 2007.
  6. Arthur Lewbel & Thomas Tao Yang, 2013. "Identifying the Average Treatment Effect in a Two Threshold Model," Boston College Working Papers in Economics 825, Boston College Department of Economics.
  7. Hans Lööf, 2009. "Multinational enterprises and innovation: firm level evidence on spillover via R&D collaboration," Journal of Evolutionary Economics, Springer, vol. 19(1), pages 41-71, February.
  8. Arthur Lewbel & Yingying Dong & Thomas Tao Yang, 2012. "Viewpoint: Comparing features of convenient estimators for binary choice models with endogenous regressors," Canadian Journal of Economics, Canadian Economics Association, vol. 45(3), pages 809-829, August.
  9. Jeremy Fox & Natalia Lazzati, 2013. "Identification of discrete choice models for bundles and binary games," CeMMAP working papers CWP04/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  10. 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.

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