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

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Fri, 24 Apr 2009 14:33:50 +0100 |

oops...well, I should have been clearer indeed. You're right that it is supposed to be growth of GDP predicting growth of Jewellery demand. 0 growth of GDP should be on pure economic grounds equal 0 growth in jewellery demand, or at least never positive - the forecast resulting from this corresponds very well with our expectations on where it would sit amongst other countries. Durbin Watson has been defined without intercept (as far as I understand), but you'd have to use a different table for the upper and lower bound. Please see here for a reference: http://www.nd.edu/~wevans1/econ30331/Durbin_Watson_tables.pdf I re-calculated R-squared with Kvalseth's preferred method (see reference below: thanks Nick), which works fine for normal OLS without a constant. I am now trying to make it work with the AR(1) regression that results from the Prais command in Stata. I have some problems with how to treat the Rho in Kvalseths formula. Any ideas? On Fri, Apr 24, 2009 at 1:10 PM, Nick Cox <n.j.cox@durham.ac.uk> wrote: > 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/ > * * 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: RE: Goodness of fit measure akin to R-squared for 0-constant or noconstant***From:*"Martin Weiss" <martin.weiss1@gmx.de>

**RE: st: RE: Goodness of fit measure akin to R-squared for 0-constant or noconstant***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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

**st: RE: Goodness of fit measure akin to R-squared for 0-constant or noconstant***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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