Re: st: Can Stata estimate Mixed Fixed and Random (MFR) model?

[Michael Ingre <Michael.Ingre@ipm.ki.se>]

Hi Binh

>  I have read
> that to avoid the bias and inconsistency caused by
> heterogeneity I should use a mixed fixed and random
> effect model.
The term "mixed" refers to the fact that both random and fixed effects 
are estimated and -xtreg- fits mixed effects models (with a random 
intercept) by default or by using the -re- option for the GLS estimator 
and the -mle- option for maximum likelihood estimation.


>  my dataset contains many missing
> observations. My question is should I drop all these
> observations for the consistent and efficient
> estimates?
If you drop the observations you not only reduce statistical power you 
also assume that data is missing completely at random (MCAR). This is a 
rather strong assumption and can usually be relaxed by maximum 
likelihood estimation to assume data only being missing at random 
(MAR). MAR does not mean that missing is random (sic!) it may be 
systematic. However, the probability of of a missing value should be 
related to the covariates in the model, not the dependent variable. 
Look at Schafer & Graham (2002) for an introduction.

I think that -xtreg y x , i(id) mle- produce valid estimates under the 
MAR assumption but I have not seen a reference to it in the manual. 
Perhaps some one else on the list can enlighten us.

Another program called -gllamm- is robust when data is MAR but it is 
slow in converging and might not be the optimal solution for you. It 
can however be downloaded directly from within Stata -ssc install 
gllamm-.

Michael

References

Schafer, J. L. and Graham, J. W. Missing data: our view of the state of 
the art. Psychological Methods 2002,7:147-177

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