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R: st: OLS assumptions not met: transformation, gls, or glm as solutions?


From   "Carlo Lazzaro" <carlo.lazzaro@tiscalinet.it>
To   <statalist@hsphsun2.harvard.edu>
Subject   R: st: OLS assumptions not met: transformation, gls, or glm as solutions?
Date   Mon, 17 Dec 2012 17:43:20 +0100

Maarten is absolutely right.
I should have stressed the difference between "solving" for
heteroskedasticity and "making this assumption irrelevant" by specifing the
-vce(robust)- option.
Besides, graphical inspection of residuals makes more sense after
transforming explanatory variables in trying to get rid of
heteroskedasticity.
Thanks for your correction and sorry for the confusion.

Kind regards,
Carlo



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On Mon, Dec 17, 2012 at 4:17 PM, Carlo Lazzaro wrote:
> The main meaning of my example is that you cannot be sure, after 
> invoking -robust-, that heteroskedasticity is automatically  removed.
> In other words, homoskedasticity should be checked graphically even after
- robust -.

Robust standard errors do not change the coefficients, just the standard
errors change. So the predicted values and residuals will also remain
unchanged after you have specified the -vce(robust)- option. The whole point
of robust standard errors is not that it "solves" in some way for
heteroskedasticity, it just makes that "assumption" irrelevant. For more,
see section 20.20 of the User's Guide.

Hope this helps,
Maarten

---------------------------------
Maarten L. Buis
WZB
Reichpietschufer 50
10785 Berlin
Germany

http://www.maartenbuis.nl
---------------------------------
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