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A Sample Selection Model for Fractional Response Variables

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  • Schwiebert, Jörg

Abstract

This paper develops a sample selection model for fractional response variables, i.e., variables taking values between zero and one. It is shown that the proposed model is consistent with the nature of the fractional response variable, i.e., it generates predictions between zero and one. A simulation study shows that the model performs well in finite samples and that competing models, the Heckman selection model and the fractional probit model (without selectivity), generate biased estimates. An empirical application to the impact of education on women's perceived probability of job loss illustrates that the choice of an appropriate model is important in practice. In particular, the Heckman selection model and the fractional probit model are found to underestimate (in absolute terms) the impact of education on the perceived probability of job loss.

Suggested Citation

  • Schwiebert, Jörg, 2016. "A Sample Selection Model for Fractional Response Variables," VfS Annual Conference 2016 (Augsburg): Demographic Change 145527, Verein für Socialpolitik / German Economic Association.
  • Handle: RePEc:zbw:vfsc16:145527
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    References listed on IDEAS

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    1. Papke, Leslie E. & Wooldridge, Jeffrey M., 2008. "Panel data methods for fractional response variables with an application to test pass rates," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 121-133, July.
    2. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, September.
    3. Esmeralda A. Ramalho & Joaquim J.S. Ramalho & José M.R. Murteira, 2011. "Alternative Estimating And Testing Empirical Strategies For Fractional Regression Models," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 19-68, February.
    4. Winkelmann, Liliana & Winkelmann, Rainer, 1998. "Why Are the Unemployed So Unhappy? Evidence from Panel Data," Economica, London School of Economics and Political Science, vol. 65(257), pages 1-15, February.
    5. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    6. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-720, May.
    7. Charles F. Manski & John D. Straub, 2000. "Worker Perceptions of Job Insecurity in the Mid-1990s: Evidence from the Survey of Economic Expectations," Journal of Human Resources, University of Wisconsin Press, vol. 35(3), pages 447-479.
    8. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    9. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-632, Nov.-Dec..
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    Cited by:

    1. Tawanda Chingozha & Dieter von Fintel, 2019. "Property rights, market access and crop cultivation in Southern Rhodesia: evidence from historical satellite data," Working Papers 03/2019, Stellenbosch University, Department of Economics.

    More about this item

    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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