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Re: st: fixed effect, autocorrelation heteroskedasticity


From   ghislain dutheil <gdutheil@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: fixed effect, autocorrelation heteroskedasticity
Date   Sun, 24 Aug 2008 15:16:49 +0200

Clive Nicholas a écrit :
> David Jacobs replied to Ghislain Dutheil:
>
>   
>> Economists often use -xtgls- and manually enter the dummies for cases (and
>> years if you need 2-way fixed effects).
>>
>> Another possibility is to use xtreg, fe and cluster on case id.  This will
>> correct the t-values for serial correlation, but you probably don't have
>> enough cases in each cross-section.  This approach might work if you enter
>> less than about 10 explanatory variables, however.
>>
>>     
I don't understand the xtreg and cluster
>> By the way, the -xtreg, fe- routine in Stata 10 will warn you if you've
>> exceeded the limits on the number of explanatory variables when you cluster
>> on case id and the matrix is not full rank. But Stata 8.2 will not.
>>     
>
> Ghislain didn't make clear how her dependent variable is measured; I
> shall assume it's continuous and without bounds.
>
> She reported that in her dataset, N=14 and T=57. If by this, she means
> that she only has 14 panel cases measured across 57 time-points
> (assuming these are equally spaced), 
yes that's the case
> then I'm pretty sure that any
> cross-sectional GLS estimates will likely be pretty unreliable as
> compared to those reported in OLS-PCSE models. Indeed, this is the
> very reason one would fit the latter kind of model to such data.
>
>   
the two model give results quite different : in one case (FGLS) an 
explonary variable is significative in the other, PCSE, it is not, and i 
have only two explonary variables... so the difference is sensible . So 
excuse me but why cross-sectional GLS estimates is pretty unreliable 
compare to OLS-PCSE ?
> Nevertheless, I would suggest fitting all three models mentioned by
> David to your data, -estout- the model output and then eyeball the
> estimates across the columns to judge which model appears closest to
> your theoretical expectations.
>
> Hope that helps.
>
>   



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