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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? > > * > > * For searches and help try: > > * http://www.stata.com/help.cgi?search > > * http://www.stata.com/support/statalist/faq > > * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ -- NEU: FreePhone - 0ct/min Handyspartarif mit Geld-zurück-Garantie! Jetzt informieren: http://www.gmx.net/de/go/freephone * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Testing to compare goodness of fit***From:*tlv101@gmx.net

**Re: st: Testing to compare goodness of fit***From:*Nick Cox <njcoxstata@gmail.com>

**RE: st: Testing to compare goodness of fit***From:*Cameron McIntosh <cnm100@hotmail.com>

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