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st: completely determined panels in xtlogit


From   Tim Wade <wadetj@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: completely determined panels in xtlogit
Date   Thu, 24 Feb 2005 12:18:33 -0500

Hello statalisters:

I have a dataset with  a binary outcome variable (hcgi) and a "group"
cluster I would like to model with random effects. The main predictor,
x1 is continuous. There are four clusters and they range in size from
about 2,000 to over 10,000 observations. When I fit an xtlogit model,
I get the response that "2 completely determined panels" or
occasionally "4 completely determined panels"  when I fit slightly
differnt models. I read the FAQ about this which says it means the
same as logistic regression when observations are completely
determined. While I understand the message in a regular logistic
model, I don't understand how a panel could be completely determined,
or what I can do to avoid this.  Any clarifications would be greatly
appreciated!

. xi: xtlogit hcgi  x1 gicontact_any i.agecat if anycontact==1, i(group) 
i.agecat1         _Iagecat1_1-5       (naturally coded; _Iagecat1_1 omitted)

Fitting comparison model:

Iteration 0:   log likelihood = -3937.4203
Iteration 1:   log likelihood = -3863.6323
Iteration 2:   log likelihood = -3846.2755
Iteration 3:   log likelihood = -3846.1091
Iteration 4:   log likelihood = -3846.1091

Fitting full model:

tau =  0.0     log likelihood = -2522.9427
tau =  0.1     log likelihood = -2516.1209
tau =  0.2     log likelihood = -2517.9593
Iteration 0:   log likelihood = -2516.1209
Iteration 1:   log likelihood = -2512.8231
Iteration 2:   log likelihood = -2512.8038
Iteration 3:   log likelihood = -2512.8033
Iteration 4:   log likelihood = -2512.8033

Random-effects logistic regression              Number of obs      =     13535
Group variable (i): beachnum                    Number of groups   =         4

Random effects u_i ~ Gaussian                   Obs per group: min =      1294
                                                               avg =    3383.8
                                                               max =      7239

                                                Wald chi2(6)       =     80.85
Log likelihood  = -2512.8033                    Prob > chi2        =    0.0000

------------------------------------------------------------------------------
        hcgi |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     x1           |   .1349034   .0622243     2.17   0.030     
.012946    .2568608
gicontact_~y |   1.342348   .1607566     8.35   0.000     1.027271    1.657426
 _Iagecat1_2 |   .0855625    .224333     0.38   0.703    -.3541221    .5252471
 _Iagecat1_3 |  -.2844456   .2464818    -1.15   0.248     -.767541    .1986498
 _Iagecat1_4 |   .0568205   .2148344     0.26   0.791    -.3642472    .4778882
 _Iagecat1_5 |  -.2423311   .2175506    -1.11   0.265    -.6687224    .1840601
       _cons |  -2.683717   .2941536    -9.12   0.000    -3.260248   -2.107187
-------------+----------------------------------------------------------------
    /lnsig2u |  -2.458037   1.163423                     -4.738303   -.1777706
-------------+----------------------------------------------------------------
     sigma_u |   .2925796   .1701969                      .0935601    .9149505
         rho |   .0253603   .0287565                      .0026537    .2028432
------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) =  2666.61 Prob >= chibar2 = 0.000

Note: 2 completely determined panels

Tim Wade
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