random effects logit model [was: st: From: Emma Gorman ...]

[Nick Cox <njcoxstata@gmail.com>]

Please use meaningful titles for your postings and full references.
Both are recommended in the Statalist FAQ.

-xt- is set up to be general enough to apply to all sorts of problems,
including but not only those with longitudinal data. -xt- is in fact
quite often applied to problems without a time variable. It is a
user's decision whether their problem requires panels of a certain
minimum length.

In any case singleton panels with known time can contribute
information about the overall change.

Nick

Emma Gorman

On Wed, Aug 24, 2011 at 3:02 AM,  <owner-statalist@hsphsun2.harvard.edu> wrote:

> I am estimating a random effects logit model, augmented with cluster
> means  to account for correlation between effects and varying
> covariates (a la Mundlak 1978), using xtlogit.
>
> xtlogit WKRT $tmeans if allyears==1, or intpoints(20) re ;
>
> Where tmeans is regular covariates + cluster means.
>
> I have three waves of data (individuals over time) and would ideally like to
> use all longitudinal respondents (those who are in all three waves in this
> analysis). However, a fair few individuals have missing data for some
> covariates, so these observations are dropped from the regression model.
>
> I initially ended up with  *minimum observation per group: 1*
>                                                     avg obs per group: 2.5
>                                                 max obs per group: 3
>
> I found that there were many individuals who were observed in all waves, but
> only had non-missing information for all covariates (and dep variable) in
> one wave. I had assumed such people, who have only one usable wave of
> observation, would be automatically dropped form estimation by Stata as they
> provide no longitudinal info for the model.
>
> So I isolated and removed these people from the estimation command manually,
> to end up with:
>
>
> Random effects u_i ~ Gaussian                   Obs per group: min =2
>                                                              avg = 2.7
>                                                              max = 3
>
>
> My question is essentially: why is it that such cases, which only provide
> cross-sectional information, not dropped automatically (/should / they be
> dropped) ? Or is there an option to only use longitudinal information? How
> is this consistent with theory? It seems strange that the default should be
> to include everyone.
>
> My understanding of random effects models is that they use the most
> efficient combination of between and within variation, the time invariant
> individual effects are integrated out of the likelihood function and are
> assumed to be independent (in the non-linear case).
>
> So we don't want to know about those who don't have longitudinal information
> for estimation. (??)
>
> NB a complication with inclusion of cluster means is that  if there
> are individuals who only have one usable wave of information due to a
> missing dependent variable for the other waves, these guys still have
> valid cluster means for the explanatory variables, so in some sense
> there is still within and between information even with just one
> 'wave' of usable information. So perhaps these guys should not be
> gotten rid of...
>
>

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