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RE: st: alternatives to fixed effects for panel data


From   Lloyd Dumont <lloyddumont@yahoo.com>
To   statalist@hsphsun2.harvard.edu
Subject   RE: st: alternatives to fixed effects for panel data
Date   Fri, 28 Dec 2007 07:07:47 -0800 (PST)

Thanks, Steven.

The paper you recommended is helpful, but as you said,
it assumes N>>T as opposed to T>N.  Are there any
other approaches, parametric or otherwise, that lend
themselves to cross-sectional time series analysis
when there are lots of time periods, but not too many
clusters?  Thanks.  Lloyd.


--- steven proud <steven_proud@hotmail.com> wrote:

> 
> 
> 
> Much of the panel data literature assumes a large
> cross sectional aspect, and a short time period
> (i.e. N>>T)
> 
> Now, if you wish to see the effect of various
> covariates, you may want to use a random effects
> model, although this is only consistent under very
> strict assumptions.
> 
> A possible other solution is using the arrellano
> bond difference gmm, or a system gmm method of
> estimation....
> 
> DYNAMIC PANEL DATA MODELS:
> A GUIDE TO MICRO DATA METHODS AND PRACTICE
> Stephen Bond
> THE INSTITUTE FOR FISCAL STUDIES
> DEPARTMENT OF ECONOMICS, UCL
> 
> has a good description of said models.
> 
> 
> > Date: Sun, 23 Dec 2007 07:42:02 -0800
> > From: lloyddumont@yahoo.com
> > Subject: st: alternatives to fixed effects for
> panel data
> > To: statalist@hsphsun2.harvard.edu
> >
> > Hello. My data structure presents what must be a
> > common problem.
> >
> > I have quarterly outcomes data on 15 units. The
> panel
> > is unbalanced. The average number of observations
> per
> > unit is about 20. But, just about all of the
> > explanatory variables that I have are
> time-constant.
> > So, at the moment, I am doing something like...
> >
> > - xtreg DEPVAR LINEARTREND INDEPVAR1 INDEPVAR2
> > INDEPVAR1xLINEARTREND
> >
> > I think a fixed-effects specification--xtreg,
> fe--is
> > called for, but it, of course, precludes my
> analysis
> > of the variables I care about. This seems like it
> > must come up a lot in situations in which one has
> a
> > small number of units/clusters, but each with a
> > reasonable (.e., more than one) number of
> observations
> > within.
> >
> > Can anyone suggest an alternative, perhaps non- or
> > semi-parametric way to address change over time in
> > these data?
> >
> > Thank you! Lloyd.
> >
> >
> >
> >
> >
>
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