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From | Joachim Landström <joachim.landstrom@fek.uu.se> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: Panel Fixed(random) effects model with autocorrelation and heteroskedasticity |
Date | Tue, 10 Nov 2009 19:55:00 +0100 |
Lucas,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.
ReferenceRoodman, 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.
Regards, Joachim Quoting Lucas Bremer <bremer@bwl.uni-kiel.de>:
Dear all, I read a lot in the stata archive, but I didn't find the right answer for my problem. 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 (http://www.stata.com/support/faqs/stat/panel.html). 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, Lucas * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/
-- Joachim Landström * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/
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