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. 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.
|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:||27. Dec 2010 19:43|
|Last Modified:||12. Feb 2013 14:28|
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