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A Binary Choice Model with Sample Selection and Covariate-Related Misclassification

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  • Jorge González Chapela

    (Academia General Militar, Centro Universitario de la Defensa de Zaragoza, 50090 Zaragoza, Spain)

Abstract

Misclassification of a binary response variable and nonrandom sample selection are data issues frequently encountered by empirical researchers. For cases in which both issues feature simultaneously in a data set, we formulate a sample selection model for a misclassified binary outcome in which the conditional probabilities of misclassification are allowed to depend on covariates. Assuming the availability of validation data, the pseudo-maximum likelihood technique can be used to estimate the model. The performance of the estimator accounting for misclassification and sample selection is compared to that of estimators offering partial corrections. An empirical example illustrates the proposed framework.

Suggested Citation

  • Jorge González Chapela, 2022. "A Binary Choice Model with Sample Selection and Covariate-Related Misclassification," Econometrics, MDPI, vol. 10(2), pages 1-20, March.
    • Handle: RePEc:gam:jecnmx:v:10:y:2022:i:2:p:13-:d:777879
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    References listed on IDEAS

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    1. Thomas Dohmen & Armin Falk & David Huffman & Uwe Sunde, 2010. "Are Risk Aversion and Impatience Related to Cognitive Ability?," American Economic Review, American Economic Association, vol. 100(3), pages 1238-1260, June.
    2. Jean-Louis Arcand & Linguère M'Baye, 2013. "Braving the waves: the role of time and risk preferences in illegal migration from Senegal," CERDI Working papers halshs-00855937, HAL.
    3. Gary S. Becker & Casey B. Mulligan, 1997. "The Endogenous Determination of Time Preference," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(3), pages 729-758.
    4. William W. Gould & Jeffrey Pitblado & Brian Poi, 2010. "Maximum Likelihood Estimation with Stata," Stata Press books, StataCorp LP, edition 4, number ml4, March.
    5. Aller, Carlos & González Chapela, Jorge, 2013. "Misclassification of the dependent variable in a debt–repayment behavior context," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 162-172.
    6. Jonathan Cohen & Keith Marzilli Ericson & David Laibson & John Myles White, 2020. "Measuring Time Preferences," Journal of Economic Literature, American Economic Association, vol. 58(2), pages 299-347, June.
    7. Maria Felice Arezzo & Giuseppina Guagnano, 2019. "Misclassification in Binary Choice Models with Sample Selection," Econometrics, MDPI, vol. 7(3), pages 1-19, July.
    8. Gibson, John & McKenzie, David, 2011. "The microeconomic determinants of emigration and return migration of the best and brightest: Evidence from the Pacific," Journal of Development Economics, Elsevier, vol. 95(1), pages 18-29, May.
    9. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Theory," Econometrica, Econometric Society, vol. 52(3), pages 681-700, May.
    10. Raven Molloy & Christopher L. Smith & Abigail Wozniak, 2011. "Internal Migration in the United States," Journal of Economic Perspectives, American Economic Association, vol. 25(3), pages 173-196, Summer.
    11. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    12. Ramalho, Esmeralda A., 2002. "Regression models for choice-based samples with misclassification in the response variable," Journal of Econometrics, Elsevier, vol. 106(1), pages 171-201, January.
    13. Bruce Meyer & Nikolas Mittag, 2013. "Misclassification In Binary Choice Models," Working Papers 13-27, Center for Economic Studies, U.S. Census Bureau.
    14. Poterba, James M & Summers, Lawrence H, 1995. "Unemployment Benefits and Labor Market Transitions: A Multinomial Logit Model with Errors in Classification," The Review of Economics and Statistics, MIT Press, vol. 77(2), pages 207-216, May.
    15. Bollinger, Christopher R & David, Martin H, 2001. "Estimation with Response Error and Nonresponse: Food-Stamp Participation in the SIPP," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 129-141, April.
    16. Butler, J S, 1996. "Estimating the Correlation in Censored Probit Models," The Review of Economics and Statistics, MIT Press, vol. 78(2), pages 356-358, May.
    17. 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.
    18. Meyer, Bruce D. & Mittag, Nikolas, 2017. "Misclassification in binary choice models," Journal of Econometrics, Elsevier, vol. 200(2), pages 295-311.
    19. Aline Bütikofer & Giovanni Peri, 2021. "How Cognitive Ability and Personality Traits Affect Geographic Mobility," Journal of Labor Economics, University of Chicago Press, vol. 39(2), pages 559-595.
    20. 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.
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