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 ?