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

[ippab ippab <ippab.statalist@gmail.com>]

Thanks a lot Maarten, Arne, and Nick.  I really appreciate the
insights you shared with me.  I will try to read more about xtmelogit
and use it.  Hopefully, I don't have to bother you again.



On Fri, Jun 4, 2010 at 4:57 AM, Arne Risa Hole <arnehole@gmail.com> wrote:
> As Maarten says it is more relevant to compare -mixlogit- with
> -xtmelogit- since they both allow for random intercepts *and* slope
> parameters while -xtlogit- allows for a random intercept only. In
> addition -mixlogit- can handle cases with more than two discrete
> outcomes, although this is not relevant for ippab's application. The
> other main difference between -mixlogit- and -xtmelogit- is that the
> integrals in the likelihood function are approximated by using
> simulation in the former case (-mixlogit-) and quadrature in the
> latter (-xtmelogit-). As pointed out by Maarten the results should be
> similar, but estimation times are likely to be faster with -mixlogit-
> when there are several random parameters in the model.
>
> I agree with Nick - a familiar line by now to those who followed the
> recent UK election - that it's always a good idea to point out when a
> command is user-written.
>
> Arne (author of -mixlogit-)
>
> On 4 June 2010 09:31, Maarten buis <maartenbuis@yahoo.co.uk> wrote:
>> --- 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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