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st: gllamm or else?
Andrea Bennett <email@example.com>
st: gllamm or else?
Fri, 27 Jun 2008 18:23:39 +0200
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,
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