Bridging logistic and OLS regression

Kapsalis, Constantine (2010): Bridging logistic and OLS regression.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: regression
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C35 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions

References

Amemiya, T. (1981). "Qualitative response models: a survey", Journal of Economic Literature 19: 1483-1536.

Goldberger, A. (1964). Econometric theory (Wiley, New York).

Moffitt, Robert (1999). “New Developments in Econometric Methods for Labor Market Analysis”, in Handbook of Labour Economics, Volume 3, Chapter 24, Edited by O. Ashenfelter and D. Card.

Pohlmann, John T. and Dennis W. Leitner (2003). “A Comparison of Ordinary Least Squares and Logistic Regression”, The Ohio Journal of Science, 103 (5): 118-125.

Theil, H. (1981). Principles of econometrics (Wiley, New York).