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From |
Bas de Goei <bas.degoei@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: R: Goodness of fit measure akin to R-squared for 0-constant or noconstant |

Date |
Fri, 24 Apr 2009 11:04:34 +0100 |

Hmm, my searches online have provided me with some insightful work by Kvalseth (1985). Apparently, he has an alternative R-squared which should work across models (including no constant or 0 constant models). It's specified by 1 - [(Y-XBhat) ' (Y-XBhat) / Y'Y - Ymean squared] I could put it in myself, or is there already a user-written command for this uniform R-squared? On Fri, Apr 24, 2009 at 10:50 AM, Carlo Lazzaro <carlo.lazzaro@tiscalinet.it> wrote: > Dear Bas, > I don't know whether or not your models (with and without constant) can be > fruitfully compared via AIC or BIC criteria. > > However, my knee-jerk advice is typing: > > - search postestimation timeseries - > > from within Stata. > > Sorry I cannot be more helpful. > > Kind Regards, > Carlo > -----Messaggio originale----- > Da: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Bas de Goei > Inviato: venerdì 24 aprile 2009 10.54 > A: statalist@hsphsun2.harvard.edu > Oggetto: st: Goodness of fit measure akin to R-squared for 0-constant or > noconstant > > Dear all, > > I am currently creating forecasts for jewellery demand in India by > regressing GDP on demand for jewellery. > > Let me first give the required background: > I have data going back to 1980. In a regression based on GDP over > time, you obviously run into the problem of serial autocorrelation, > though this is neccesarily a problem for a forecast, my boss wants > "only regressions that pass Durbin Watson test". > > I really have two problems: > > The first is that the normal OLS regression result indicated a > positive intercept. However, economically this would mean that even > when there is no growth in GDP, there would still be growth in the > demand for jewellery. Of course, there was the problem that the model > did not pass the Durbin Watson test. Fitting the model with the GLS > approach (the prais command in Stata), did improve the model, but it > kept (as expected) the intercept positive. > > I decided to inspect the data more closely, and to drop two outliers > from the data. The intercept under the Prais command is now still > positive, but it has become insignificant. I decided that there is > justification to re-run the regression with a 0 intercept. However, > this balloons the F statistic and the R-squared. I now understand why > that is, given the mathematics behind the R squared calculation. > > My question is, how would you calculate in Stata a "correct" or > "alternative" R-squared, or a goodness of fit measure, which you can > use to compare it to the model with a constant?? > > Thanks!! > > Bastiaan > * > * 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/ > * * 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/

**Follow-Ups**:**AW: st: R: Goodness of fit measure akin to R-squared for 0-constant or noconstant***From:*"Martin Weiss" <martin.weiss1@gmx.de>

**References**:**st: Goodness of fit measure akin to R-squared for 0-constant or noconstant***From:*Bas de Goei <bas.degoei@gmail.com>

**st: R: Goodness of fit measure akin to R-squared for 0-constant or noconstant***From:*"Carlo Lazzaro" <carlo.lazzaro@tiscalinet.it>

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