Bridging logistic and OLS regression
AbstractThere 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 27706.
Date of creation: Apr 2010
Date of revision: Dec 2010
Other versions of this item:
- C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-01-03 (All new papers)
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- Moffitt, Robert A., 1999. "New developments in econometric methods for labor market analysis," Handbook of Labor Economics, Elsevier, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 24, pages 1367-1397 Elsevier.
- Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, American Economic Association, vol. 19(4), pages 1483-1536, December.
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