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Re: st: st)why log linear model is better?
Thanks for the comments. Both are helpful
----- Original Message -----
From: "Nick Cox" <email@example.com>
Sent: Tuesday, August 29, 2006 6:10 AM
Subject: RE: st: st)why log linear model is better?
> 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.
> 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.
> > > 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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