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Re: st: Panel Fixed(random) effects model with autocorrelation and heteroskedasticity

From   Joachim Landström <>
Subject   Re: st: Panel Fixed(random) effects model with autocorrelation and heteroskedasticity
Date   Tue, 10 Nov 2009 19:55:00 +0100

It sounds as if a PCSE would be in place but such models are asymptotic in T and not in N (in other words does such models rest on having "small" N/T-ratios)which means that it is out of the question for you (since your N/T-ratio is "large"). You need a model that is asymptotic in N and which can treat autocorrelation and heteroscedasticity.

I suggest that first you do a Prais-Winsten transformation of your variables to remove any presence of autocorrelation. Then you might add a time dummy to reduce the likelihood of panel-wide heteroscedasticity. I think Roodman (2008) discusses the use of time dummies to remove panel-wide heteroscedasticity. If you suffer from heteroscedasticity on id-level you may have to consider to also deflate your variables (i.e., if they still are on levels).

However, you do not specify your model so here I have to make a guess. If you use a lagged dependent variable in your model, you really must use a dynamic panel regression model since your FE model most likely suffers (badly) of Nickell bias (too small T). That brings you into the use of e.g. xtabond or similar models.

Roodman, D. 2008. How to do Xtabond2: An Introduction to Difference and System GMM in Stata. In Center for Global Development Working Paper No. 103: Center for Global Development.


Quoting Lucas Bremer <>:

Dear all,

I read a lot in the stata archive, but I didn't find the right answer for my

In fact, I have two panel datasets (each 700 obs, 6 time periods). So first
of all I tested random vs. fix effects with the hausman test. For one Panel
I should use random effects, for the other one fixed effects. Then I tested
for serial correlation and heteroskedasticity with positive results

Now I search for the right estimation procedure to handle serial correlation
& heteroskedasticity for random effects and for fixed effects.

Is there a procedure that can do the corrections for fe & re? I think it is
better to use the same estimation procedure in both datasets and do not
switch between them to be able to compare the results.

I tried to use xtgls ... , panels(hetero) corr(ar1) for the random effects
model. For the fixed effects model I added for each ID a personal dummy, but
that didn't worked.

Thank you in advance,

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Joachim Landström

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