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 <[email protected]> wrote:
> <>
>
> This formula screams "MATA"!
>
> See http://www.stata.com/meeting/fnasug08/baum_StataMata.beamer.FNASUG08.pdf
>
>
>
> HTH
> Martin
>
>
> -----Ursprüngliche Nachricht-----
> Von: [email protected]
> [mailto:[email protected]] Im Auftrag von Bas de Goei
> Gesendet: Freitag, 24. April 2009 12:05
> An: [email protected]
> 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
> <[email protected]> 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: [email protected]
>> [mailto:[email protected]] Per conto di Bas de Goei
>> Inviato: venerdì 24 aprile 2009 10.54
>> A: [email protected]
>> 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/
