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Bridging logistic and OLS regression

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  • Kapsalis, Constantine

Abstract

There is broad consensus that logistic regression is superior to ordinary least squares (OLS) regression at predicting the probability of an event. However, OLS is still widely used in binary choice models, mainly because OLS coefficients are more intuitive than logistic coefficients. This paper shows a simple way of calculating linear probability coefficients (LPC), similar in nature to OLS coefficients, from logistic coefficients. It also shows that OLS coefficients tend to be very close to logistic LPC coefficients.

Suggested Citation

  • Kapsalis, Constantine, 2010. "Bridging logistic and OLS regression," MPRA Paper 25482, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:25482
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    References listed on IDEAS

    as
    1. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
    2. Moffitt, Robert A., 1999. "New developments in econometric methods for labor market analysis," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 24, pages 1367-1397, Elsevier.
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    More about this item

    Keywords

    regression;

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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