# st: RE: xtlogit (re) and logit: same results

 From "Andreas Kamp" <[email protected]> To <[email protected]> Subject st: RE: xtlogit (re) and logit: same results Date Tue, 19 Jul 2005 10:37:12 +0100

```Dear Stata-Users,

as I still haven't found an answer to my question, let me try to precise
my question.

As I mentioned before, I am trying to estimate default probabilities
using a pooled logit model (logit) and a random effects panel logit
model (xtlogit, re). Results for both models are perfectley the same.

Here are the results for xtlogit, re:

Fitting comparison model:

Iteration 0:   log likelihood =  -1377.398
Iteration 1:   log likelihood = -1342.3038
Iteration 2:   log likelihood = -1329.8824
Iteration 3:   log likelihood = -1328.7468
Iteration 4:   log likelihood = -1328.7417
Iteration 5:   log likelihood = -1328.7417

Fitting full model:

tau =  0.0     log likelihood = -1328.7417
tau =  0.1     log likelihood = -1330.1846

Iteration 0:   log likelihood = -1328.7417
Iteration 1:   log likelihood = -1328.7417

Random-effects logistic regression              Number of obs      =
19895
Group variable (i): sysnr                       Number of groups   =
3164

Random effects u_i ~ Gaussian                   Obs per group: min =
1
avg =
6.3
max =
8

Wald chi2(7)       =
101.89
Log likelihood  = -1328.7417                    Prob > chi2        =
0.0000

------------------------------------------------------------------------
------
ausfall |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+----------------------------------------------------------
------
mas1_2 |  -1.363757   .6244236    -2.18   0.029    -2.587605
-.1399094
ejb128_2 |  -.2314217   .0711395    -3.25   0.001    -.3708526
-.0919908
ekq_2 |  -18.38955    6.88451    -2.67   0.008    -31.88294
-4.896155
gkr_2 |  -129.3035   26.23809    -4.93   0.000    -180.7292
-77.87775
pi_2 |  -1.447272   .6023384    -2.40   0.016    -2.627833
-.2667099
notl_2 |   2.162819   .3786747     5.71   0.000      1.42063
2.905008
geno |   .5634791   .2215662     2.54   0.011     .1292174
.9977408
_cons |   .9511411   1.598035     0.60   0.552    -2.180949
4.083232
-------------+----------------------------------------------------------
------
/lnsig2u |  -14.99999   585.2423                     -1162.054
1132.054
-------------+----------------------------------------------------------
------
sigma_u |   .0005531   .1618446                      4.6e-253
6.6e+245
rho |   9.30e-08   .0000544                             0
.
------------------------------------------------------------------------
------
Likelihood-ratio test of rho=0: chibar2(01) =     0.00 Prob >= chibar2 =
1.000

I read somewhere that a model might not converge because it has too few
defaults (dependend variable = 1). However, I don't understand this
argument. Why does the model not converge if it has to few observations
equalt to 1?

Andreas

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```