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Re: st: Using mixlogit as a substitute of xtlogit.


From   Maarten buis <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Using mixlogit as a substitute of xtlogit.
Date   Fri, 4 Jun 2010 01:31:22 -0700 (PDT)

--- On Fri, 4/6/10, ippab ippab wrote:
> I am wondering if there is any benefit of using mixlogit
> for a binary dependent variable in a panel data. 
> Basically, to use mixlogit, I will have to create two
> alternatives (which are complementary), an alternative
> specific constant, and interactions between independent
> variables and the alternative specific constant.  Is
> this is a wise thing to do?

No, you should get very similar results, and the differences
are due to particularities of the maximizing algorithms, so
have no substantive interpretation.

> My vague understanding is that mixlogit allows for more
> heterogeneity than xtlogit. 

Not true, the value of -mixlogit- is that it allows one to
estimate a random effects model within a multinomial logit.

> But, I am confused about interpreting a population average
> beta obtained from xtlogit in light of estimates from
> mixlogit.  Just to give an example, if the estimate
> for x1 is 2.05 from xtlogit, the interpretation would be
> that increasing x1 increases the the likelihood of y=1. 
> Now, getting a similar estimate from mixlogit, e.g., mean
> is 2.5 for x1 with std 3.10, makes the interpretation
> complicated.  This means for about 20.9% of the sample,
> increasing x1 does not increase the likelihood of
> y=1.  Am I understanding these correctly?  

Yup, and now it is more clear to me why you are comparing
with -mixlogit-. I guess that you were interested in making
some of the coefficients random as well. If that is the case
then you should have used -xtmelogit- instead.

> The more critical question is, if more than 20% or 30% of
> the sample have different preference, what does it mean to
> have a positive significant coefficient (mean)?

It means that people behave differently to the same stimulus,
but that on average the effect is positive. A possible next
step would be to try to explain this variation by adding 
interactions between higher level variables and the lower
level variable x1.

Hope this helps,
Maarten

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany

http://www.maartenbuis.nl
--------------------------


      

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