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Estimation in Binary Choice Models with Measurement Errors

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Author Info

  • Edgerton, David

    ()
    (Department of Economics, Lund University)

  • Jochumzen, Peter

    ()
    (Department of Economics, Lund University)

Abstract

In this paper we develop a simple maximum likelihood estimator for probit models where the regressors have measurement error. We first assume precise information about the reliability ratios (or, equivalently, the proxy correlations) of the regressors. We then show how reasonable bounds for the parameter estimates can be obtained when only imprecise information is available. The analysis is also extended to situations where the measurement error has non-zero mean and is correlated with the true values of the regressors. An extensive simulation study shows that the estimator works very well, even in quite small samples. Finally the method is applied to data explaining sick leave in Sweden.

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Bibliographic Info

Paper provided by Lund University, Department of Economics in its series Working Papers with number 2003:4.

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Length: 64 pages
Date of creation: 16 Apr 2003
Date of revision: 07 Jul 2003
Handle: RePEc:hhs:lunewp:2003_004

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Contact details of provider:
Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund,Sweden
Phone: +46 +46 222 0000
Fax: +46 +46 2224613
Web page: http://www.nek.lu.se/en
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Related research

Keywords: Measurement error; errors-in-variables; probit; binary choice; bounds;

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References

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  1. Hsiao, Cheng & Wang, Q Kevin, 2000. "Estimation of Structural Nonlinear Errors-in-Variables Models by Simulated Least-Squares Method," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 41(2), pages 523-42, May.
  2. Kao, Chihwa & Schnell, John F., 1987. "Errors in variables in a random-effects probit model for panel data," Economics Letters, Elsevier, vol. 24(4), pages 339-342.
  3. Klepper, Steven & Leamer, Edward E, 1984. "Consistent Sets of Estimates for Regressions with Errors in All Variables," Econometrica, Econometric Society, vol. 52(1), pages 163-83, January.
  4. Murphy, Kevin M & Topel, Robert H, 2002. "Estimation and Inference in Two-Step Econometric Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 88-97, January.
  5. Li, Tong, 2002. "Robust and consistent estimation of nonlinear errors-in-variables models," Journal of Econometrics, Elsevier, vol. 110(1), pages 1-26, September.
  6. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843 Elsevier.
  7. Amemiya, Yasuo, 1985. "Instrumental variable estimator for the nonlinear errors-in-variables model," Journal of Econometrics, Elsevier, vol. 28(3), pages 273-289, June.
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Citations

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Cited by:
  1. Christopher J. Ruhm & Alison Snow Jones & William C. Kerr & Thomas K. Greenfield & Joseph V. Terza & Ravi S. Pandian & Kerry Anne McGeary, 2011. "What U.S. Data Should be Used to Measure the Price Elasticity of Demand for Alcohol?," NBER Working Papers 17578, National Bureau of Economic Research, Inc.
  2. Dhawan, Rajeev & Jochumzen, Peter, 1999. "Stochastic Frontier Production Function With Errors-In-Variables," Working Papers 1999:007, Lund University, Department of Economics.
  3. Hvide, Hans K & Panos, Georgios, 2013. "Risk tolerance and entrepreneurship," CEPR Discussion Papers 9339, C.E.P.R. Discussion Papers.

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