--- In [email protected], lm0542 wrote:
> Dear Stata Experts,
> I would like to estimate a model of occupational
> choice of university graduates, taking as endogenous
> each of the following 3 sequential decision nodes:
>
> 1) choice of university course (polychotomous variable
> with 1 to K groups, e.g. economics, law, humanities
> etc..)
> 2) choice on labour market participation (binary
> variable: 1 = employed, 0 = non employed)
> 3) choice of occupation (polychotomous variable with 1
> to J groups, e.g. craft, menial, blue-collar, white
> collar workers etc.)
>
> I was thinking to use GLLAMM for this purpose but
> since I'm new to the program I'm not sure whether it
> might be suitable.
> Thank you very much in advance for any suggestion.
I am not much of an expert, either, but one thing I know is that
unless you have the latest Cray at your disposal, the model should be
kept to a moderate size. The model you are trying to put forward will
have something like (K-1)*1*(J-1) correlated random effects, and the
time to converge is exponential in this number, as it involves
integration over the real space of that dimension. If you have a
dataset of say 10000 individuals, and you also want to take some
sample design clustering into account... you are doomed to wait for a
few weeks for the model to converge. (It took a few days with
-oprobit- link and the panel structure with just one random effect on
my computer.) Another computational difficulty is an artefact of the
multidimensional integration. You would want to have a good resolution
where the joint density is supported, but as far as you would have to
have a grid of points, it means that you should have a lot of
integration points in each of the dimensions, and the number of the
integration points is in fact the base of the exponent I mentioned before.
Chapter 9 of the manual does have the example of the -gllamm-
equivalent / extension of -mlogit-, but you also would need to look at
the multiple equations models with (outdated) -eq- command in Chapters
4 and 5.
Stas
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