Andreas,
Your rho is zero. The random effects model IS the pooled model.
-Jo
Jyotsna (Jo) Puri
[email protected]
----Original Message Follows----
From: "Andreas Kamp" <[email protected]>
Reply-To: [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?
Thank you for your help,
Andreas
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