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SV: st: GLAMM and nested logit/probit


From   "Kristian Karlson" <kristian.karlson@gmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   SV: st: GLAMM and nested logit/probit
Date   Tue, 18 Nov 2008 23:37:12 +0100

Sorry about that! Maarten it is ;)
/Kristian

-----Oprindelig meddelelse-----
Fra: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] På vegne af Martin Weiss
Sendt: 18. november 2008 23:34
Til: statalist@hsphsun2.harvard.edu
Emne: Re: st: GLAMM and nested logit/probit

If only I knew as much as m_AA_rten about -gllamm-, I would be glad :-)

HTH
Martin
_______________________
----- Original Message ----- 
From: "Kristian Karlson" <kristian.karlson@gmail.com>
To: <statalist@hsphsun2.harvard.edu>
Sent: Tuesday, November 18, 2008 11:31 PM
Subject: RE: st: GLAMM and nested logit/probit


> Dear Martin,
>
> I have used the gllamm manual quite a lot for multilevel multinomial and
> ordinal logit models and linear multilevel growth model with latent 
> classes
> for the unobserved heterogeneity. I have, however, not found a 
> presentation
> of how to model a nested multinomial logit model, hence this thread on the
> statalist.
>
> (For clarification: Yes, you get it right - the idea was to model the 
> latent
> class membership. How specifically to do this I haven't decided on yet.
> Sorry about being unclear about the identification issue. My point here 
> was
> a mere indication that a more realistic model with transition varying
> covariates would be "better", i.e. not depend critically on distributional
> assumptions of the errors. I am not an expert in identification theory, so
> I'd better read some more about before I mention it again ;) The problem
> about intervening variables is also unclear in my study; I'll have to look
> at this more thoroughly).
>
> Nonetheless, my problem here is technical. Maybe I need to go through the
> gllamm manual once again (and this time read it more in depth). However, 
> if
> anyone can give me a helping hand with the estimation of the nested
> multinomial logit model in -gllamm- stated in the top of this thread, I
> would be thankful.
>
> All the best,
> Kristian
>
>
>
> -----Oprindelig meddelelse-----
> Fra: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] På vegne af Maarten buis
> Sendt: 18. november 2008 23:06
> Til: statalist@hsphsun2.harvard.edu
> Emne: Re: st: GLAMM and nested logit/probit
>
> --- Kristian Karlson <kristian.karlson@gmail.com> wrote:
>> The main point of the study is to delve into what the unobserved
>> components in educational decisions consist of, i.e. describing the
>> latent classes that account for unobserved heterogeneity with
>> variables that indicate different forms of motivation.
>
> Just for clarification: You have observed variables that are indicators
> of a latent variable, and you are interested what part of the
> unobserved heterogeneity can be captured by this latent variable?
>
> Have you already found www.gllamm.org, which has the manual and lots of
> example code?
>
>> The main point is that the more complex a model (i.e. the more
>> realistic model of the educational system), the better the
>> identification of the unobserved heterogeneity influencing
>> educational decisions net of respondent background characteristics
>> (family background, gender, ability, etc.).
>
> I am not sure I buy this point.
>
> First, I always think of identification in terms of where the
> information you are using in your estimation comes from: is the data,
> the design, or your assumptions. Better identification for me means
> that you are making better use of the observed data or the study
> design, rather than assumptions. More complex models typically mean
> that you are making more use of assumptions.
>
> Second, controlling for variables is not an aim in it's own right, but
> a means of controlling confounding variables. So, a clear (theoretical)
> idea about which variables are confounders and which variables are
> intervening variables is crucial. So controlling for background
> characteristic does _not_ necessarily lead to better estimates, it
> depends on the place of these variables in the causal chain.
>
> -- Maarten
>
> -----------------------------------------
> 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/
> -----------------------------------------
>
>
>
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