Dino,
Give your -mlogit- results, too; did you feed them as starting values
to -gllamm-? Keep in mind that introducing random effects changes the
scaling of your coefficients; see
http://www.citeulike.org/user/ctacmo/article/3057661.
I'd fully agree with earlier reactions that your model does not appear
to be identified well. Correlation of 1 are troublesome; in your
particular case this may mean that there is no difference between two
of the three alternatives -- is this making sense? If one of the
categories is quite rare, or present in some weird patterns wrt units
producing random effects, it may create those numeric problems, too.
On 11/24/08, KONSTANTARAS KONSTANTINOS <[email protected]> wrote:
> Dear statalisters,
>
> In my model runs (using Intercooled Stata 9.2) I experience tremendous
> instability in gllamm results, higher than has been elsewhere reported at
> statalist: I run a simple multinomial logit model with random effects, using
> the results as initial matrix for exactly the same model, to get as a result
> an extremely different log likelihood and coefficients etc. The resulting
> loglikelihood differs by large margins (3-9%). I have also tried to use
> exactly the same initial values from mlogit but not only does the overall
> loglikelihood continues to be unstable but also the same happens with the
> loglikelihood obtained without random effects –running gllamm with init
> option-. Nevertheless the model invariably converges, albeit to an entirely
> different estimate.
--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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