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From |
"Stanislav Kolenikov" <skolenik@yahoo.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: question on GLLAMM |

Date |
Thu, 19 Aug 2004 14:53:21 -0000 |

--- In statalist@yahoogroups.com, 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the -gllamm- equivalent / extension of -mlogit-, but you also would need to look at the multiple equations models with (outdated) -eq- command in Chapters 4 and 5.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 * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: question on GLLAMM***From:*Michael Ingre <Michael.Ingre@ipm.ki.se>

**References**:**st: question on GLLAMM***From:*lm0542 <lm0542@yahoo.com>

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