Re: st: non-normal residuals

[Maarten Buis <maartenlbuis@gmail.com>]

Non-Gaussianity(*) of the residuals is on its own almost never a real
problem. Linear regression(**) is well known to be remarkably robust
in that sense. What you need to take care of is whether or not your
effects are linear, as strong deviations from Gaussianity can be a
sign that that might be a problem. But as long as your effects are
reasonably linear, you either do nothing about non-Gaussianity or you
specify -vce(robust)- or you specify -vce(bootstrap)-. I'd be worried
if the method matters.

Notice that -ladder- or -gladder- do not look at residuals but at the
marginal distribution, so those commands cannot solve your problem.

-- Maarten

(*) Gaussian distribution is another name for the "normal"
distribution without the normative connotation.

(**) OLS is the name of the algorithm used for estimating the
parameters of a linear regression model. So the name of the model you
want to estimate is "linear regression model" not "OLS".

On Mon, Feb 11, 2013 at 11:22 PM, Xixi Lin <winnielxx@gmail.com> wrote:
> Hi All,
>
> I have a cross-sectional multiple regression, I wanna use OLS, and
> tested for its assumptions. It turned out that the residuals are not
> normal. And I tried to transform Y and X to other forms, however, it
> does not help to solve the non-normality problem. I use the following
> code to find the best fit for transformation.
>
> ladder Y
> gladder Y
>
> Is there any other way that I can fix the non-normality problem? Thanks.
>
> Best,
> Xixi Lin
> *
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-- 
---------------------------------
Maarten L. Buis
WZB
Reichpietschufer 50
10785 Berlin
Germany

http://www.maartenbuis.nl
---------------------------------
*
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*   http://www.ats.ucla.edu/stat/stata/