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Het gebruik van een parametrische en een semi-parametrische schattingsmethode voor het binaire keuzemodel: Probit Maximum Likelihood versus Maximum Score
[The use of a parametric and a semi-parametric estimation method for the binary choice model: Probit Maximum Likelihood versus Maximum Score]

Author

Listed:
  • Peeters, H.M.M.

Abstract

This Master thesis investigates the semi-parametric estimation method Maximum Score of Manski (1988) that can be used to estimate binary choice models. This method only asumes that the median of the disturbances of the econometric model takes the value zero. We compare Maximum Score with the semi parametric estimation method of Maximum Likelihood, that is based on the explicit assumption of normality of the the distribution of the disturbances. We proceed in three steps. First, the two estimation methods are compared theoretically. Second, the use of bootstrap methods is explained for the calculation of standard errors and confidence intervals for the Maximum Score estimators. Third, empirical applications are estimated and the results of both estimation methods are compared. One main conclusion of this research is that Maximum Score should be used in case of uncertainty about the disturbances' distribution and in case of large samples. A drawback of Maximum Score is that the estimators converge rather slowly. Moreover, one of the explanatory variables in the binary choice model must be continuous.

Suggested Citation

  • Peeters, H.M.M., 1989. "Het gebruik van een parametrische en een semi-parametrische schattingsmethode voor het binaire keuzemodel: Probit Maximum Likelihood versus Maximum Score [The use of a parametric and a semi-paramet," MPRA Paper 28104, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:28104
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    References listed on IDEAS

    as
    1. Kapteyn, Arie & Wansbeek, Tom & Buyze, Jeannine, 1980. "The dynamics of preference formation," Journal of Economic Behavior & Organization, Elsevier, vol. 1(2), pages 123-157, June.
    2. Robinson, P M, 1988. "Semiparametric Econometrics: A Survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 3(1), pages 35-51, January.
    3. Manski, Charles F. & Thompson, T. Scott, 1986. "Operational characteristics of maximum score estimation," Journal of Econometrics, Elsevier, vol. 32(1), pages 85-108, June.
    4. Arie Kapteyn & Peter Kooreman & Rob Willemse, 1988. "Some Methodological Issues in the Implementation of Subjective Poverty Definitions," Journal of Human Resources, University of Wisconsin Press, vol. 23(2), pages 222-242.
    5. Bera, Anil K & Jarque, Carlos M & Lee, Lung-Fei, 1984. "Testing the Normality Assumption in Limited Dependent Variable Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(3), pages 563-578, October.
    6. 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.
    7. Chesher, Andrew & Irish, Margaret, 1987. "Residual analysis in the grouped and censored normal linear model," Journal of Econometrics, Elsevier, vol. 34(1-2), pages 33-61.
    8. Ruud, Paul A, 1983. "Sufficient Conditions for the Consistency of Maximum Likelihood Estimation Despite Misspecifications of Distribution in Multinomial Discrete Choice Models," Econometrica, Econometric Society, vol. 51(1), pages 225-228, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Binary choice ; Maximum Likelihood ; Maximum Score ; bootstrapping ; parametric ; semi parametric ;
    All these keywords.

    JEL classification:

    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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