st: gllamm or else?

[Andrea Bennett <mac.stata@gmail.com>]

Dear Statalisters,

I'm pretty new to panel data (I'm trying to cope with it by reading  
"Multilevel and Longitudinal Modeling Using Stata" , http://www.stata.com/bookstore/mlmus2.html) 
. I have two data sets while in both of them I use one binary variable  
(yes/no) and one ordered categorical variable (0, 1, 2) as dependent  
variables. Now, I've been playing around with probit/xtprobit/gllamm  
and oprobit/gllamm estimations for the former and the latter case.  
Both data sets contain information based on random samples (but the  
people filling in the questionnaire are different for each year), e.g.  
for the first data set I have a persistent survey structure relate to  
the very same topic while the survey was performed in four different  
years. In the second data set I also have a persistent survey  
structure but there is more then one topic per year as well as the  
related topic is different for each year (it's a survey asking people  
about their opinion on political issues). Though having seen this,  
from what I understand it is not really appropriate to use reg/xtreg  
in these cases.

What I am not sure about is how I should perform the estimation. From  
a theoretical standpoint, I would assume fixed effects in the first  
data set while for the second data set it is an open question, still  
(I could group for political main topics, for example). Now, in OLS I  
simply would include year dummies for fixed effects. But as far as I  
know, I should not use year dummies in gllamm (and usually not in  
probit/oprobit) for estimating fixed effects.

So, a) how would I include a simple fixed effect estimation in gllamm  
(as I understand it, using i(year) applies random effects) and b) how  
would I deal with year fixed effects and random effects for the  
different topics. Additionally, which gllamm options are best suited  
for these kind of estimation? Should I use -adapt- and should I set a  
specific value for -nip- ? Using only the -adapt- option (which I read  
results in a better estimation) already results in 15min estimation  
time for a sub-sample of about 3000 observations while the full data  
set contains 25'000 observations. I really fear this will take forever  
(besides that my computer dies from overheating!).

Many many thanks to everybody who can give me some good advise (maybe  
I do not need gllamm?) or just shares her/his experiance on this topic!


Kind regards and many thanks for the consideration,

Andrea


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