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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

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

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

Date |
Fri, 24 Apr 2009 13:10:05 +0100 |

I don't understand the substantive reasoning here, as regressing GDP on demand for jewellery seems a backward way to predict the latter. Perhaps "on" has a differing meaning here. Or perhaps you mean GDP growth and jewellery demand growth: your posting appears contradictoru on this and in any case is not very clear to me. On general grounds the origin of zero GDP and zero jewellery demand would seem likely to be a long way away from the bulk of the data! On one very specific and one very general technical point: My recollection is that the Durbin-Watson test is only defined for a model with intercept, but I can't find chapter and verse for that possibly garbled memory. Although its title is not your exact question, the material in FAQ . . . . . . . . . . . . . . . . . . . . . . . Do-it-yourself R-squared . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. J. Cox 9/03 How can I get an R-squared value when a Stata command does not supply one? http://www.stata.com/support/faqs/stat/rsquared.html has much bearing on your situation. It pushes various simple ideas. Here's one: in many models, and yours seems to be among them, it is simple and natural to think of correlation between observed and predicted or its square as one measure of model merit. Naturally, _no_ single measure can ever tell the complete story. Nick n.j.cox@durham.ac.uk P.S. later contributions to this thread mentioned a paper without ever giving a proper full reference. Here it is: Kv{\aa}lseth, T.O. 1985. Cautionary note about $R^2$. American Statistician 39: 279-285. Bas de Goei =========== 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?? * * 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: RE: 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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