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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. OLS is still widely used in binary choice models because its coefficients are easier to interpret, while the resulting estimates tend to be close to the logit estimates anyway. Although some statistical software provide an easy way of calculating marginal effects (equivalent in interpretation to OLS coefficients) this is not always the case. This paper shows a simple way of calculating marginal effects from logistic coefficients.

Suggested Citation

  • Kapsalis, Constantine, 2010. "Bridging logistic and OLS regression," MPRA Paper 27706, University Library of Munich, Germany, revised Dec 2010.
  • Handle: RePEc:pra:mprapa:27706
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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 analysis;

    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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