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From |
"Carlo Lazzaro" <carlo.lazzaro@tiscalinet.it> |

To |
<statalist@hsphsun2.harvard.edu> |

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

Date |
Fri, 24 Apr 2009 11:50:25 +0200 |

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/

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

**References**:**st: 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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