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11-20-2012 01:55 PM

Hi!

I set up a standard gamma regression in PROC GENMOD as follows:

PROC GENMOD DATA = &inds. (where = (&dv. > 0));

MODEL &dv. = &ivlist. / dist = gam;

OUTPUT OUT = &outds. p = pred_&dv.;

RUN;

and got the following response:

WARNING: The specified model did not converge.

ERROR: Error in computing deviance function.

NOTE: The scale parameter was estimated by maximum likelihood.

I then attempted to perform a standard OLS on this data (I just changed

the dist= value from "gam" to "nor"), and that converged successfully.

I assumed from standard practice that a gamma regression was appropriate

for this data (it's a typical insurance loss model). I did not transform

the dependent variable (or the regressors) beforehand.

Any ideas / feedback are welcome.

Thanks!

-- TMK --

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Posted in reply to topkatz

11-21-2012 08:48 AM

I would seriously consider changing the link from the default inverse to the log. I know it is not the canonical link, but it does restrict the parameters to a "proper" space. That is why the default link for the gamma distribution in PROC GLIMMIX is the log link.

Also, it is likely that the convergence criteria need some fiddling--more iterations are the most likely solution.

Steve Denham

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Posted in reply to SteveDenham

11-26-2012 01:38 AM

Thank you for the response! That's an interesting observation about the link function, I wonder why GLIMMIX uses a different default for the gamma? But I may try the log when I get the chance (I'm going to try the ITPRINT option first to see if I can get any more diagnostic info).

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Posted in reply to topkatz

11-26-2012 09:43 AM

Not sure if I have this right, but I think the log link will result in a constant coefficient of variation across values, while the inverse may not. My friend Google seems to think that a gamma distribution with a log link is a lot more common for econometric data, so if that is what you are looking at, it may be helpfult to switch. I would also strongly recommend searching the listserv archives for SAS-L. See what you can find from Dale McLerran and David Cassell.

Steve Denham