Re: st: Question on PCSE vs FGLS and serial correlation

[Austin Nichols <austinnichols@gmail.com>]

Joshua Linder <jl0924a@student.american.edu> :
The cluster-robust estimator produces SEs robust to arbitrary serial
correlation as long as N is reasonably large.  If you also have
contemporaneous cross-sectional correlation, see
http://www.stata.com/statalist/archive/2010-02/msg00338.html

On Tue, Mar 2, 2010 at 4:37 PM, Joshua Linder
<jl0924a@student.american.edu> wrote:
> Hi,
>
> Some guidance on the below inquiry would be much appreciated as I am having
> a hard time finding someone who is faimiliar with the PCSE and FGLS
> methodology.  Thank you very much for your help.
>
>
> I have been using the PCSE regression method to analyze my time-series
> cross-sectional data; however of the three panel error assumptions
> (groupwise heteroskedasticity, contemporaneous correlation, and serial
> correlation), the PCSE method only corrects for the first two.  I tested for
> serial correlation using the Wooldridge xtserial test.  Thus the problem of
> serially correlated errors needs to be addressed before running the PCSE
> model or it can be addressed by specifying PCSE-AR1 in Stata (indicating
> presence of autocorrelation) and a Prais-Winsten regression will then be
> used to correct for the serial correlation. If I try to correct serial
> correlation by including a lagged dependent variable or by specifying it in
> the model function (corrAR1) my results change completely and most
> relationships are no longer significant.  My guess is that this is an
> over-correction and really isn't necessary; however I cannot justify this
> based on my level of stats knowledge or from related literature.
>
>  Complicating matters is that I have also tried running the regression using
> the FGLS method which supposedly accounts for all three of the panel error
> assumptions; however based on literature especially by Beck and Katz, this
> method seems to only be appropriate when T is at least three times as large
> as N (for my data T is twice as large as N).  Also, most recent studies
> using panel data prefer to employ the PCSE method.  Oddly though, the FGLS
> and PCSE methods produce identical results in Stata when autocorrelation is
> not specified, which from my perspective implies that PCSE method is also
> doing some sort of correction for serial correlation making a further
> correction unnecessary.  The problem comes in explaining the methodology of
> the study.  Since PCSE is the preferred methodology I do not want to include
> a lagged dependent variable to correct for serial correlation or specify AR1
> in the function because this leaves me with basically no significant
> findings to discuss.  The results from just running PCSE or FGLS on the
> other hand produce several significant coefficients that we can explore.  Is
> there a way to justify not specifying AR1 or to justify using FGLS? or
> perhaps another way to correct for serial correlation that may not have a
> huge impact on the results?
>
> -Josh

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