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Re: RE: st: Testing to compare goodness of fit


From   tlv101@gmx.net
To   statalist@hsphsun2.harvard.edu
Subject   Re: RE: st: Testing to compare goodness of fit
Date   Tue, 04 Oct 2011 23:08:06 +0200

Hi cam

the question is whether WTI oil price or LLS oil price is a better predictor for US GDP growth. So including both simultaneously doesn't really make much sense.

-------- Original-Nachricht --------
> Datum: Tue, 4 Oct 2011 16:57:44 -0400
> Von: Cameron McIntosh <cnm100@hotmail.com>
> An: STATA LIST <statalist@hsphsun2.harvard.edu>
> Betreff: RE: st: Testing to compare goodness of fit

> Also, why not estimate a single model with X and Z jointly predicting Y?
> If the predictors are correlated, it would seem to me that you would need to
> include both of them in the model in order to get unbiased estimates.  I
> guess I'm thinking in parallel process growth curve terms (i.e., X, Z and Y
> having co-evolving trajectories)... I'm not sure what framework you're
> implementing this in and what it allows in term of multiple predictors of the
> trend.
> Cam 
> 
> > Date: Tue, 4 Oct 2011 21:51:07 +0100
> > Subject: Re: st: Testing to compare goodness of fit
> > From: njcoxstata@gmail.com
> > To: statalist@hsphsun2.harvard.edu
> > 
> > Relying on R-sq alone is not a good idea.
> > 
> > Goodness of fit can be compared by
> > 
> > 1. Plotting the two sets of predictions in time.
> > 1a. Plotting the two sets of residuals in time.
> > 
> > 2. Looking for autocorrelation in residuals.
> > 
> > 3. Scatter plots of observed vs predicted in each case.
> > 3a. Residual vs predicted plots.
> > 
> > One maxim is never to use a R-sq without inspecting the corresponding
> > scatter plot. Another is that a good model is associated with
> > pattern-free residuals.
> > 
> > If the models look equally good, there is likely to be some scientific
> > reason to discriminate between them.
> > 
> > Nick
> > 
> > On Tue, Oct 4, 2011 at 9:35 PM,  <tlv101@gmx.net> wrote:
> > 
> > I have two univariate time series models, both explaining variable Y,
> > one with variable X and one with variable Z as the explanatory
> > variable (plus a constant). Now, both models yield an R-squared that
> > is rather close to each other. Can I really say that model X is better
> > than model Z just by comparing these R-squareds (since with 5
> > observation more or less, things might look different)? Or can I test
> > whether these r-squareds are statistically different from each other?
> > Any other idea to evaluate goodness of fit in that case, except for
> > comparing RMSE? Or is in this case comparing (f-testing) the
> > coefficients of X and Z helpful?
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