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Binary Response Models: Logits, Probits and Semiparametrics

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  • Joel L. Horowitz
  • N. E. Savin

Abstract

A binary-response model is a mean-regression model in which the dependent variable takes only the values zero and one. This paper describes and illustrates the estimation of logit and probit binary-response models. The linear probability model is also discussed. Reasons for not using this model in applied research are explained and illustrated with data. Semiparametric and nonparametric models are also described. In contrast to logit and probit models, semi- and nonparametric models avoid the restrictive and unrealistic assumption that the analyst knows the functional form of the relation between the dependent variable and the explanatory variables.

Suggested Citation

  • Joel L. Horowitz & N. E. Savin, 2001. "Binary Response Models: Logits, Probits and Semiparametrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 43-56, Fall.
    • Handle: RePEc:aea:jecper:v:15:y:2001:i:4:p:43-56
    Note: DOI: 10.1257/jep.15.4.43
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    File URL: http://www.aeaweb.org/articles.php?doi=10.1257/jep.15.4.43
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    References listed on IDEAS

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    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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