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

From   Michael Ingre <[email protected]>
To   [email protected]
Subject   Re: st: Can Stata estimate Mixed Fixed and Random (MFR) model?
Date   Tue, 12 Oct 2004 15:57:39 +0200

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
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-.



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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