What do you think the issues may be? Or how may sample size come into play?
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
What do you think the issues may be? Or how may sample size come into play?
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What type of estimates do you get when running linear and logistic models?
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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.
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.
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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 ?
So for clarification, your question is what are the disadvantages of using a linear regression model when your dependent variable is binary, correct?
Stop cowardice, ban guns!
Yes, and the advantages. Same for the ML.
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