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RE: st: st)why log linear model is better?


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: st)why log linear model is better?
Date   Tue, 29 Aug 2006 13:10:17 +0100

The same applies, even more strongly, to 
interpretation of standard errors, which 
are no longer on the same scale. 

In addition, look at diagnostic plots
such as residual vs fitted, in each case. 

Nick 
n.j.cox@durham.ac.uk 

David Greenberg
 
> 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. 

Woong.Chung@colorado.edu
 
> > 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

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