Kapsalis, Constantine (2010): Bridging logistic and OLS regression.
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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|
|Original Title:||Bridging logistic and OLS 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|
|Depositing User:||Constantine Kapsalis|
|Date Deposited:||02. Oct 2010 21:35|
|Last Modified:||13. Feb 2013 08:42|
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