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: [email protected]
> Subject: st: alternatives to fixed effects for panel data
> To: [email protected]
>
> 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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