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st: R: cluster() and robust option


From   Gianni Virgili <gianni.virgili@unifi.it>
To   statalist@hsphsun2.harvard.edu
Subject   st: R: cluster() and robust option
Date   Tue, 16 Aug 2005 18:17:15 +0200

Hi, you could use glm y x1 x2...,  fam(bin) link(logit) cluster(id)
robust
Gianni

-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Kit Baum
Inviato: marted́ 16 agosto 2005 17.20
A: statalist@hsphsun2.harvard.edu
Oggetto: st: cluster() and robust option

Jamie said

I am afraid I don't know the answer to your questions as I am a very new
STATA user. However, I note that you talk about the sandwich / Huber /
White variance estimator. I am working with a national dataset and a
binary outcome. Many of the small areas that I am investigating have
only one respondent.

I am using xtlogit to investigate area effects and I am wondering
whether the sandwich estimator improves the estimates of the effects and
whether I should / could use this with xtlogit?


I do not believe that robust or vce(robust) is an option compatible  
with xtlogit using the default random-effects specification:

. xtlogit bin mvalue kstock, robust
robust invalid
r(198);

I don't have my full documentation handy, but I do not think sandwich  
is available here.

Not my area of expertise, but if you have geographic areas with small  
number of respondents per area, shouldn't you be using survey methods  
such as svy: logit ? Unless you have a timeseries from a single  
respondent xtlogit is surely not the right tool for the job. But if  
you have some information about the sampling of individuals from  
regions, it would seem that the svy: commands would be appropriate.

Kit Baum, Boston College Economics
http://ideas.repec.org/e/pba1.html


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