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A Comparison Of Binary Logit And Probit Models With A Simulation Study

Author

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  • Selen CAKMAKYAPAN

    (Hacettepe University, Department of Statistics)

  • Atilla GOKTAS

    (Mugla Sıtkı Kocman University, Department of Statistics)

Abstract

Logit and probit models which widely used are members of the family of generalized linear models. Particularly, when the dependent variable is binary, both models may be used for the estimation of the functional relationship between dependent and independent variables. Since those models are utilized for the same purposes, the question of which model performs better comes to the mind. For this intention, a Monte Carlo simulation was carried out to compare both the binary probit and logit models under different conditions. In data generation stage, by employing latent variable approach, different sample sizes, different cut points, and different correlations between dependent variable and independent variables were taken into account. To make a comparison between logit and probit models, residuals, deviations and different Pseudo-R squares which are used for qualitative data analysis, were calculated and the results were interpreted.

Suggested Citation

  • Selen CAKMAKYAPAN & Atilla GOKTAS, 2013. "A Comparison Of Binary Logit And Probit Models With A Simulation Study," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 2(1), pages 1-17, JULY.
    • Handle: RePEc:aes:jsesro:v:2:y:2013:i:1:p:1-17
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    References listed on IDEAS

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    1. Michael Veall & Klaus Zimmermann, 1994. "Evaluating Pseudo-R 2 's for binary probit models," Quality & Quantity: International Journal of Methodology, Springer, vol. 28(2), pages 151-164, May.
    2. Veall, Michael R & Zimmermann, Klaus F, 1996. "Pseudo-R-[superscript 2] Measures for Some Common Limited Dependent Variable Models," Journal of Economic Surveys, Wiley Blackwell, vol. 10(3), pages 241-259, September.
    3. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
    4. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
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    Cited by:

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    More about this item

    Keywords

    Binary Logit Model; Binary Probit Model; Latent Variable; Monte Carlo Simulation; Pseudo R-Square;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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