st: RE: Serial Correlation

[DE SOUZA Eric <eric.de_souza@coleurope.eu>]

See inline comments

Eric


Eric de Souza
College of Europe
Brugge (Bruges), Belgium
http://www.coleurope.eu



-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Robert Mills
Sent: 04 February 2011 16:47
To: statalist@hsphsun2.harvard.edu
Subject: st: Serial Correlation

Run my regression in OLS, then used the Breush-Pagan Lagrange Multiplier Test, which rejected the null hypothesis that the variance of errors is zero (homoskedastic), thus OLS is inconsistent so I need to use Random or Fixed Effects

          - if heteroscedasticity is the only problem then OLS is not inconsistent but the standard errors and , therefore, inference are invalid.


I've used a Hausman Test in which determined Random effects to be inconsistent, so I'm going to use Fixed Effects.

         - in the presence of heteroscedasticity the standard Hausman test is not valid. Use the user written (Baum, Schaffer, Stillman, Wiggins) -overid- test. This test can also be run even after robust estimation

So my errors are heteroskedastic, and I need to correct for this - do I simply use robust standard errors in Stata? Or should I use the Huber-White Standard Errors? Or are these the same thing?

        - the Huber-White s.e. take account of heteroscedasticity only. The robust option for OLS  is equivalent

I've read that using Huber-White Standard Errors requires no serial correlation in error terms. To check for this, I need to perform a Durbin-Watson Test, and if I find serial correlation, use Prais-Winsten (GLS) to correct this.

     -  the Durbin-Watson test, like the standard Hausman test, requires stringent assumptions.

     - if you estimate a fixed effects model, the heteroscedasticity consistent standard errors are inconsistent. This is why the robust option in recent versions of Stata take into account both heteroscedasticity and serial correlation within groups.

However, can you use GLS for fixed effects? And if so, how do you do this in Stata?

Or, should I use Newey West Standard Errors, which correct for both heteroskedasticity and for serial correlation (AR 1). This would seem like the best option, but I'm not sure if you can use NW SE's for fixed effects? If so, how is this done in stata?

Thanks in advance for any help you may have!

Cheers,

Robert Mills



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