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Re: st: mim: xtmixed for unconditional models

From   Nailing Xia <>
Subject   Re: st: mim: xtmixed for unconditional models
Date   Thu, 23 Apr 2009 11:28:24 -0400

Maarten, you are right. I tried to estimate using the unimputed
dataset with the command:

xtmixed dvar if [parwgt] || schid: || childid:,

and it has the same problem (no standard errors for random
components). The model I am estimating has three levels: time, child
(using the variable "childid"), and school (using "schid"). However,
children are not entirely nested within schools, because children
switch schools over time. So I tried the crossed random effects models

 xtmixed irtm if [parwgt] || _all: R.schid || childid:

But it gives error message like this:

                     J():  3900  unable to allocate real <tmp>[38153,2413]
      _xtm_mixed_ll_uu():     -  function returned error
       _xtm_mixed_ll_u():     -  function returned error
        _xtm_em_iter_u():     -  function returned error
          _xtm_em_iter():     -  function returned error
                 <istmt>:     -  function returned error

Now I am not sure how to specify the random effects at the school
level since neither nested model nor crossed model seems to work. Any


On Thu, Apr 23, 2009 at 10:26 AM, Maarten buis <> wrote:
> --- Nailing Xia wrote:
>> Thanks, Maarten. I did what you suggested, and it happens that when
>> estimating the third imputed dataset, it keeps doing the iterations -
>> showing "Iteration xx: log restricted-likelihood = -xx.xx (backed up)"
>> again and again. I guess this is the sign for the model being
>> unidentified in the third dataset?
> Yes
>> However, even if I drop the third imputed dataset, there is another
>> problem with other imputed datasets. The result only includes
>> coefficient and standard error for the intercept, and coefficients for
>> the random components, but no standard errors for the random effects
>> parameters. It also shows a warning, saying that "convergence not
>> achieved; estimates are based on iterated EM". Is this also an
>> identification problem? What can I do about it?
> That just looks like there is something very wrong with your -xtmixed-
> model. I would focuss of fixing that before doing a multiple imputation
> model. My first suspect would be the two levels of nesting: are they
> correct, or did something go wrong while preparing the data? Do you
> need both levels? Once you got -xtmixed- to work with the unimputed
> data, try it again on the imputed data. If it still doesn't work, check
> the imputation model. Again my prime suspect would be the nesting: did
> the model properly acount for the nested structure? Did it do something
> weird to the level identifiers?
> -- Maarten
> -----------------------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
> -----------------------------------------
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