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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 11:57:43 +0100 |

Cheers from the UK! On Fri, Apr 24, 2009 at 11:25 AM, Martin Weiss <martin.weiss1@gmx.de> wrote: > <> > > > I do think that such a -relatively- easy formula could be calculated in the > old -matrix- commands (see -help matrix-). All it takes would be transposing > the (Y-XBhat) matrix, subtraction and multiplication. The -help matrix- for > Stata 10 contains an example for the OLS estimator calculation, that reads > as > > *** > matrix beta = invsym(X'*X)*X'*y > *** > > so that is pretty much what you need... > > HTH > Martin > > > -----Ursprüngliche Nachricht----- > Von: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Bas de Goei > Gesendet: Freitag, 24. April 2009 12:20 > An: statalist@hsphsun2.harvard.edu > Betreff: Re: st: R: Goodness of fit measure akin to R-squared for 0-constant > or noconstant > > Ah...yes you're right. Y would be a (n x 1) vector...hmm, I only have > Stata 8, any idea how y'd do such a calc without mata? > > On Fri, Apr 24, 2009 at 11:13 AM, Martin Weiss <martin.weiss1@gmx.de> wrote: >> <> >> >> This formula screams "MATA"! >> >> See > http://www.stata.com/meeting/fnasug08/baum_StataMata.beamer.FNASUG08.pdf >> >> >> >> HTH >> Martin >> >> >> -----Ursprüngliche Nachricht----- >> Von: owner-statalist@hsphsun2.harvard.edu >> [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Bas de Goei >> Gesendet: Freitag, 24. April 2009 12:05 >> An: statalist@hsphsun2.harvard.edu >> Betreff: Re: st: R: Goodness of fit measure akin to R-squared for > 0-constant >> or noconstant >> >> 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/ >> >> >> * >> * 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/ > * * 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: 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>

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

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

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