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


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
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