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. 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 25482.
Date of creation: 28 Apr 2010
Date of revision:
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-2010-10-09 (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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