YOu have to be careful in comparing R-square for a regression in which
the dependent variable has been transformed with one in which it has not
been transformed. The dependent variables are not measured on the same
scale, and this can throw off the comparison. IF it does turn out that
the equation with transformed variables provides a better fit, the
explanation will not be a statistical one, but a substantive one. The
equation with transformed variables better describes the processes at
work. Only someone with a knowledge of those processes could offer an
explanation as to why that is. David Greenberg, Sociology Department,
New York University
----- Original Message -----
From: [email protected]
Date: Monday, August 28, 2006 7:15 pm
Subject: st: st)why log linear model is better?
> I need a help to find out reasonable explanation for my model
> specification.After running simple linear regression using OLS,
> ROBUST standard errors(due to
> heteroskadasticty) and SUR, it turned out that log linear regression:
> log(y)=a1+a2log(x1)+a3log(x2)+a4log(x3)+...e
> seems to be fit so well in any cases rather than level or other
> transformationregressions:
> y=a1+a2x1+a3x2+a4x3+.....+e
>
> in terms of lower standard errors and higher R squares.
>
> I am looking some explanations why this happens and also want to
> know how tell
> whether the log linear regression method is my best specification
>
> Mostly y x2 x3 are ratio and x1 is level( but x1 is not a
> denominator of other
> ratios)
> Within my knowledge, the log transformation would be helpful for
> multiplicativedata set. I don't know it would be applied to my case
>
> Any comments and suggestions will be welcomed and helpful
>
> Thanks
>
> WT
> *
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>
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