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st: Question on PCSE vs FGLS and serial correlation


From   Joshua Linder <jl0924a@student.american.edu>
To   statalist@hsphsun2.harvard.edu
Subject   st: Question on PCSE vs FGLS and serial correlation
Date   Tue, 2 Mar 2010 16:37:56 -0500

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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