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Re: st:Transformation for skewed variables with negative values?


From   Joseph Coveney <jcoveney@bigplanet.com>
To   Statalist <statalist@hsphsun2.harvard.edu>
Subject   Re: st:Transformation for skewed variables with negative values?
Date   Wed, 16 Aug 2006 01:50:41 +0900

Woong Chung wrote:

I need following help. I have panel dataset for estimating a simple linear
equation.
The problem is that my all variables have sknewness and big variation(large
std).
In particualr, the dependent variable and one of independant variables have
a negative sknewness, while all other independant variables are shown by
positive sknewness. My first intension is using a log transformation of all
variables but seems not to be a good idea since all variables have negative
values (around 20%)
Besides, all variables except one of independant variables are ratio, thus
that idea would make worse.

I would be so glad if anyone has suggestions to solve this problem

--------------------------------------------------------------------------------

It's not clear that you actually have a problem.

It shouldn't be a problem that your independent variables are skewed or have a wide distribution. There isn't any assumption their distribution, and it is considered better to for them to cover more ground. They're only assumed not to comprise a linear combination within machine precision. (There are other assumptions about them, in particular, about their relation to the random effects, but that's another matter.)

Fit the model as-is. Examine the residuals and empirical Bayes predictions.
If these do not have a reasonably normal-appearing distribution, then
transform the dependent variable in accordance with shaping-up their
distributions, and not the dependent variable's distribution per se.

Also, from your description, it seems that your dependent variable is a ratio. Consider sticking its denominator in the model as a predictor and using its numerator as the dependent variable.

Joseph Coveney

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