Re: st: Fixed Effects estimation with time-invariant variables

[Darby Jack <djack@fas.harvard.edu>]

I think something analogous to the SAS command you reference is  
implemented in Stata 9 as -xtmixed-

From the online help:

 -xtmixed- fits linear mixed models.  Mixed models are characterized  
as containing both fixed effects and random effects.  The fixed  
effects are analogous to standard regression coefficients and are  
estimated directly.  The random effects are not directly estimated,  
but summarized according to their estimated variances and  
covariances.  Random effects may take the form of either random  
intercepts or random coefficients, and the grouping structure of the  
data may consist of multiple levels of nested groups.  The error  
distribution of the linear mixed model is assumed to be Gaussian.

On Jul 26, 2005, at 11:12 AM, martinez@soc.duke.edu wrote:


> Joana:
> The model you are trying to apply is generally known as a mixed  
> model. It is possible to use time invariant variables and what  
> would be the equivalent of a fixed effect model in SAS, the  
> procedure is called Proc mixed.  The terminology is not the same  
> (random and fixed can mean different things), but it basically  
> allows you to create an intercept for each country, and then  
> "extract" from that intercept the explanatory power of your  
> variables.  Concerning your question about random and fixed  
> effects, there is a theory reason and a mathematical one.  The  
> theory reason for random effects is that the relationship between  
> the two variables it�s different for each country (basically, a  
> different beta), while the fixed effect assumes just a change in  
> the intercept for each country.  The mathematical reason for using  
> random effects is that the independent variables you are using are  
> not correlated with belonging to a particular country (the country  
> you belong to does not change the !
>  probability of having a particular value in one of your  
> independent variables).  This is a strong assumption (called  
> orthogonal).  If you use random effects under conditions in which  
> the country determines, even partially, the value of your  
> independent variables, then you will have specification bias and  
> your results will not be thrustworthy.  There is also the GLAMM  
> procedure in STata, but I never had good luck with it (it requires  
> too much processing power).
>
> Hope this helps.
>
> Martha Martinez.
>
>
>
>
>
> Quoting Joana Quina <joana.quina@gmail.com>:
>
>
>
> > Thank you Mark.  I was told that Hausman and Taylor have an
> > Econometrica paper where they tackle the problem of time-invariant
> > variables and fixed-effects. Are you familiar with this?
> > Regarding whether it is acceptable to use random effects, my
> > supervisor has a strong view that in this case it would be wrong.
> > Could you tell me why you say it is not obviously wrong to do so?
> >
> > Thank you again,
> > Joana
> >
> > On 26/07/05, Mark Schaffer <M.E.Schaffer@hw.ac.uk> wrote:
> >
> > > Joana,
> > >
> > > Date sent:              Tue, 26 Jul 2005 13:56:55 +0100
> > > From:                   Joana Quina <joana.quina@gmail.com>
> > > To:                     statalist@hsphsun2.harvard.edu
> > > Subject:                st: Fixed Effects estimation with time- 
> > > invariant
> > variables
> >
> > > Send reply to:          statalist@hsphsun2.harvard.edu
> > >
> > >
> > > > Fixed Effects estimation with time-invariant variables
> > > >
> > > > I have a problem and I wonder whether anyone can help. I have a  
> > > > panel
> > > > dataset of 32 Sub-Saharan African countries and four time periods
> > > > (N=32, T=4). I am looking at the determinants of aid for these
> > > > countries. Given that they are not randomly selected countries,  
> > > > I am
> > > > using Fixed Effects. However, I have a few time-invariant variables
> > > > that are important but that get dropped because of using FE.  Is  
> > > > there
> > > > any way of obtaining estimates for these time-invariant  
> > > > variables and
> > > > still use FE?
> > > No.  The fixed effects are perfectly collinear with the time-
> > > invariant variables.  There's nothing you can do about this if you
> > > want to use fixed effects.
> > >
> > >
> > > > Also, given the data I'm using, is there any
> > > > justification for using Random Effects?
> > > It's not obviously wrong to try random effects, especially as you  
> > > can
> > > use a Hausman test to check whether random effects can be accepted.
> > > The fact that the time-invariant variables aren't included in the FE
> > > estimation doesn't prevent you from using the test.
> > >
> > > Hope this helps.
> > >
> > > Cheers,
> > > Mark
> > >
> > >
> > >
> > > > Any comments would be much appreciated.
> > > >
> > > > Thanks.
> > > > Joana
> > > >
> > > > *
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> > > Prof. Mark E. Schaffer
> > > Director
> > > Centre for Economic Reform and Transformation
> > > Department of Economics
> > > School of Management & Languages
> > > Heriot-Watt University, Edinburgh EH14 4AS  UK
> > > 44-131-451-3494 direct
> > > 44-131-451-3296 fax
> > > 44-131-451-3485 CERT administrator
> > > http://www.sml.hw.ac.uk/cert
> > >
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>
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