xtreg-fe should not be used if the model is dynamic because the error
term is correlated with the endogeneous variable.
refer to Weinhold D.(1999) "A dynamic fixed effects model for
heterogeneous panel data" for using dynamic MFR. She also provide the
code for dynamic MFR but it is in GAUSS.
For causality test using MFR refer to:
Nair-Rechert, U and Weinhold. D (2001) Causality test for cross
country panels: a new look at FDI and economic growth in developing
countries" Oxford Bulletin of Economic and Statistics pp153-171.
I think you can also use the xtabond for dynamic panel and use the
result to test for causality.
--- In [email protected], Stas Kolenikov <skolenik@g...>
wrote:
> 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@i...> 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
> *
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