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 7:21 PM, Nick Cox wrote:

> I'll take (2).
> 
> You need to think about what it would mean in your field to have
> perfect predictions. It means that the data provide a complete
> description _and_ the model captures the generating process exactly.
> Or it would mean that you were using a model with too many parameters.
> In most observational fields even large datasets provide only partial
> data and the model is at best a caricature of the underlying process.
> 
> Imagine for example that the response variable was the number of
> children born so far to a set of women. We can get variables like age,
> occupation, religious affiliation, etc. that may have a variety of
> direct or more likely indirect effects on the response, but it seems
> likely that there will always be a lot of unexplained variation
> arising from individual details.
> 
> Literature in your field will tell you what kind of fit is considered
> strong enough to be publishable.
> 
> Nick
> 
> On Fri, Mar 25, 2011 at 4:46 AM, Francisco Rowe <frowe@ucn.cl> wrote:
> 
>> 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?
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