Dis./Avantadges of estimating of binary response model with OLS

In the Maximum Likelihood Estimator context, I can't find answers for those two questions:
- What are the advantages and disadvantages of estimating of a binary response model with OLS ?
- Same questions but compared to MLE ?

I thank you in advance :eek:
Well if we have a binary model, OLS will just get 0 and 1 and it will not be as precise as if it was more precise value, right ? If we have a big sample, OLS wouldn't be able to show the variety of the different values.

I'm supposed to get a consistent and if possible efficient estimates when I run a model as far as I know.


Less is more. Stay pure. Stay poor.
Well one kicks out average values and the other kick out log(odds). Two completely different types of numbers and not inter-changeable. I think my sample size comment is moot, I was thinking of Poisson regression.
Alright, I see your point. What I have up to now are the following:

+ of ML
- Consistency, efficiency, asymptotic normality
- Unifiied, general theory of estimation and testing

- of ML
- Need to specify the distribution
- Danger of Misspecification
- All results are asymptotic (why is that a problem ? )

+ of Linear Probability Model:
- Simple OLS estimation (with robust s.e.)
- Straightforward interpretation of parameters
- Other linear methods can be easily applied

- of LPM
- Heteroskedastic s.e.
Predictions sometimes outside the unit interval

Do you agree with that ?


Less is more. Stay pure. Stay poor.
So for clarification, your question is what are the disadvantages of using a linear regression model when your dependent variable is binary, correct?