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RE: st: Testing to compare goodness of fit


From   Cameron McIntosh <cnm100@hotmail.com>
To   STATA LIST <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Testing to compare goodness of fit
Date   Tue, 4 Oct 2011 16:57:44 -0400

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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