Re: st: R-squared measures proposed by Cameron and Windmeijer (1996) in stata

[Francisco Rowe <frowe@ucn.cl>]

Regarding question 1, I did something, but I would like to know if I am alright.
I ran a NBRM2 using nbreg. Then with fixed alpha parameter, I ran it again using the glm command in order to get the deviance of that model. After, I applied the same procedure for a model with the variables in my model.  Thus, using the deviances for these two model with only constant and variables, I calculated a Pseudo R-squared as 1-(Deviance for model with variables/Deviance for the only-constant model).

Is this the way how this measure of fit should be calculated in Stata?

I would appreciate your comments.

On 25/03/2011, at 2:46 PM, Francisco Rowe wrote:

> Hi,
> 
> I have two questions regarding the estimation of a Negative Binomial Regression Model with quadratic variance (NBRM2). 
> 
> 1) How could the R-squared for NBR2 based on deviances proposed in the paper below be implemented using STATA? Does it require any changes if robust standard errors are used for the estimation?
> Cameron, C and Windmeijer, F 1996, 'R-squared measures for count data regression models with applications to health-care utilization', Journal of Business & Economic Statistics, vol. 14, no. 2, pp. 209-20.
> 
> 2) In general -for what I have seen-, why measures of fit in form of (Pseudo) R-squared are relatively low (<0.3)? Are there any special conditions that cause this?
> 
> I would appreciate your comments.
> 
> Francisco Rowe.
> University of Queensland
> *
> *   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/