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Re: st: indicators of nesting versus xtmelogit


From   Maarten buis <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: indicators of nesting versus xtmelogit
Date   Wed, 22 Oct 2008 09:47:10 +0100 (BST)

Using dummies to estimate a fixed effects model is not recomended in a
non-linear model (especially if you have few observations in each
category, and 17 does not seem large enough to me). This point is
discussed here: 
http://www.stata.com/statalist/archive/2003-09/msg00103.html .

So that precludes models 2 and 3. Model 1 is not without problems
either: it assumes that the city and time specific intercepts are
uncorrelated with the observed variables, which bothers quite a few
people. If I had to choose one model I would probably go for model 1,
as being the lesser of three evils.

However, you will probably want to look at what is called a
crossed-effects model, which can be estimated with -xtmelogit-. There
is a discussion on how to do that in the XT manual on pages 239-242. 

Hope this helps,
Maarten

--- Noah Friedkin <friedkin@soc.ucsb.edu> wrote:

> The data I am analyzing are longitudinal, consisting of individuals  
> located in 4 cities across 17 time periods. I.e., each individual  
> contributes 17 observations, and each observation occurs in one of  
> 4X17=68 settings. I am not concerned with the effects of these  
> settings, but instead with a set of individual-level predictors x1,  
> x2, x3 of the  binary responses y of individuals in their city-time  
> contexts. There are three credible approaches to the analysis of
> these  
> observations:
> 
> (1)  xtmelogit y x1 x2 x3 || city: || time:
> (2) logit y x1 x2 x3 c2 c3 c4 t2 t3  ? t17, where c2-c4 and t2-t17
> are  
> indicator variables with city 1 and time 1 as the omitted settings
> (3) logit y x1 x2 x3 c1t2 c1t3 ? c1t17 c2t1 c2t2 ? c2t17 ? c4t1 c4t2 
> 
> ?c4t17 where c_t_ are indicator variables with c1t1 as the omitted  
> setting
> 
> Model 2 is a popular approach. Model 3 appears closer to model 1.  
> However, my sense is that models 2-3 are excessively conservative and
>  
> potentially misleading in allowing (what may be viewed as)
> meaningless  
> ?nuisance? associations to affect the estimates for x1, x2, and x3.  
> Model 1 seems preferable.
> 
> Question 1. What are the pros and cons of these three models?
> 
> I am assuming that model 1 is equivalent to allowing different  
> intercepts for each sity-time group of observations. I'm also
> assuming  
> that the estimated random effects for the 68 city-time groups are not
>  
> constrained, so that (for example) they might be found to be all  
> positive values that monotonically increase with time.
> 
> Question2. Are both of my assumptions about model 1 correct, or is my
>  
> understanding of model 1 flawed?
> 
> Comments on the above two questions would be much appreciated.
> 
> 
> 
> 
> 
> 
> 
> 
> 
> 
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
> 


-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands

visiting address:
Buitenveldertselaan 3 (Metropolitan), room N515

+31 20 5986715

http://home.fsw.vu.nl/m.buis/
-----------------------------------------


      
*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
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