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

From   Maarten buis <>
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 L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen


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