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Re: st: Non-normally distributed dependent variable for Random-Intercept Model and 2SLS


From   Christian Weiß <mail@cweiss.org>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Non-normally distributed dependent variable for Random-Intercept Model and 2SLS
Date   Mon, 9 Nov 2009 17:50:53 -0500

Hi Marteen,

thank you very much for your answer! However, afaik, the OLS
estimation itself does not require a normally distributed dependent
variable.

However, I thought - and please forgive me citing wikipedia at this
point - that calculation of the Confidence Interval assumes a normal
distribution?

Snip: "The calculation of a confidence interval generally requires
assumptions about the nature of the estimation process – it is
primarily a parametric method – for example, it may depend on an
assumption that the distribution of the population from which the
sample came is normal. As such, confidence intervals as discussed
below are not robust statistics, though modifications can be made to
add robustness – see robust confidence intervals."

Best
Christian




On Mon, Nov 9, 2009 at 5:33 PM, Maarten buis <maartenbuis@yahoo.co.uk> wrote:
> --- On Mon, 9/11/09, Christian Weiß wrote:
>> I am using a dependent variable which is almost, but
>> according to the respective tests, not normally distributed
>> (even after transforming).
>
> This is a very common mistake: These models do not assume that the
> dependent variable is normally distributed, but that the residuals
> are normally distributed. Any test of normality/Gaussianity of the
> dependent variable is thus meaningless.
>
> Hope this helps,
> Maarten
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
> http://www.maartenbuis.nl
> --------------------------
>
>
>
>
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