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From |
Stas Kolenikov <skolenik@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Tue, 12 Oct 2004 11:01:47 -0400 |

If you really have dynamic panels with strong time series effect (and it looks so once you talk about causality -- is it Granger causality that you want to test?) then both -xtreg, re- and -gllamm- will be of little help as they assume independence of observations taken at different time points conditional on the random effect. The focus of the causality tests is to build a time series model and see the strength of the relations between two time series. Yes, -xtreg, [re|mle]- implicitly assumes MAR and provides consistent and asymptotically efficient estimates (as MLE would, from the general missing data theory, provided your model is correctly specified). The reason it is not documented properly is that MAR/MCAR/NMAR concepts are used in sociology, while panel data models are written mostly for economists, so there is a lack of communication between the two industries. On Tue, 12 Oct 2004 15:57:39 +0200, Michael Ingre <michael.ingre@ipm.ki.se> wrote: > 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 -- Stas Kolenikov http://stas.kolenikov.name * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Can Stata estimate Mixed Fixed and Random (MFR) model?***From:*Michael Ingre <Michael.Ingre@ipm.ki.se>

**References**:**st: Can Stata estimate Mixed Fixed and Random (MFR) model?***From:*binh Nguyen <binhappp@yahoo.com>

**Re: st: Can Stata estimate Mixed Fixed and Random (MFR) model?***From:*Michael Ingre <Michael.Ingre@ipm.ki.se>

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