0

When is it appropriate to use linear regression for a binary outcome?

I understand that it is conventional to use logistic regression for a binary outcome because it generates a linear list of outcomes which avoids the problem of generating estimates greater than 1 or 0. However my professor advised me to use a linear model to predict my outcome. She said that it is actually quite common in Economics to do this. Does anyone know why using OLS regression could be deemed appropriate?

goldisfine
  • 626
  • 7
  • 16
  • 2
    Hi @Sam Finegold and welcome to the site. As you have correctly said, the standard approach if you have a binary outcome is logistic regression. If you use OLS for this kind of data, this is called [Linear Probability Model](http://courses.umass.edu/pubp608/lectures/l22-2.pdf). The coefficients express the change in probability that $Y=1$ for a unit change in $X$. But this approach has shortcomings, e.g. that the predictions are outside the range of 0, 1. And there is heteroskedasticity by design. – COOLSerdash May 31 '13 at 21:19
  • 1
    I frankly don't see what advantages Linear Probability Models have compared to logistic regression. I'd stick to logistic regression as this deals naturally with the shortcomings of an OLS with a binary outcome. – COOLSerdash May 31 '13 at 21:27

0 Answers0