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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

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.I have read that to avoid the bias and inconsistency caused by heterogeneity I should use a mixed fixed and random effect model.

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.my dataset contains many missing observations. My question is should I drop all these observations for the consistent and efficient estimates?

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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**Follow-Ups**:**Re: st: Can Stata estimate Mixed Fixed and Random (MFR) model?***From:*Stas Kolenikov <[email protected]>

**References**:**st: Can Stata estimate Mixed Fixed and Random (MFR) model?***From:*binh Nguyen <[email protected]>

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