Dear Statlist,
Here the regular Probit. Thanks a lot. As you both suggested, these are
the results:
probit newproc normscapab n5a e6 d12b Le8c if a4b==1
Iteration 0: log likelihood = -105.70439
Iteration 1: log likelihood = -102.18133
Iteration 2: log likelihood = -101.90586
Iteration 3: log likelihood = -101.19701
Iteration 4: log likelihood = -100.49243
Iteration 5: log likelihood = -100.46574
Iteration 6: log likelihood = -100.46567
Probit regression Number of obs =
165
LR chi2(5) =
10.48
Prob > chi2 =
0.0628
Log likelihood = -100.46567 Pseudo R2 =
0.0496
------------------------------------------------------------------------------
newproc | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
normscapab | 1.208397 .6709446 1.80 0.072 -.1066302
2.523424
n5a | 9.75e-09 1.41e-08 0.69 0.490 -1.79e-08
3.74e-08
e6 | .0228493 .3045747 0.08 0.940 -.5741061
.6198047
d12b | -.0028756 .005975 -0.48 0.630 -.0145863
.0088352
Le8c | 4.50e-06 2.75e-06 1.64 0.101 -8.76e-07
9.89e-06
_cons | -.1643681 .6537495 -0.25 0.801 -1.445694
1.116957
------------------------------------------------------------------------------
Note: 0 failures and 1 success completely determined.
At 11:45 AM 4/30/2009, jverkuilen wrote:
I second Rich's advice. 165 observations is a pretty small N for
heteroscedastic probit. But I suspect that even regular probit won't find
much either Look at the initial set of iterations to get starting
values---the log-likelihood only changes a little.
Good point. Based on the LLs, the probit model should have a model
chi-square of about 6 with 3 d.f. -- but that might be enough for at least
one variable to sneak in as significant.
-------------------------- Messaggio originale ---------------------------
Oggetto: [Fwd: Heteroskedastic probit]
Da: [email protected]
Data: Gio, 30 Aprile 2009 5:27 pm
A: [email protected]
--------------------------------------------------------------------------
Dear Statilist,
Richard, normscapab is not dichotomy. I constructed it as a score so it
varies from 0 and 1.You are right that then it has small value compared
with the other variables...
I tried Marteen's advice and obtained these results:
hetprob newproc normscapab e6 d12b if a4b==1, het(normscapab)
Fitting probit model:
Iteration 0: log likelihood = -105.70439
Iteration 1: log likelihood = -102.70201
Iteration 2: log likelihood = -102.69757
Iteration 3: log likelihood = -102.69757
Fitting full model:
Iteration 0: log likelihood = -102.69757
Iteration 1: log likelihood = -102.51681
Iteration 2: log likelihood = -102.41164
Iteration 3: log likelihood = -102.39453
Iteration 4: log likelihood = -102.39175
Iteration 5: log likelihood = -102.39174
Heteroskedastic probit model Number of obs =
165
Zero outcomes =
56
Nonzero outcomes =
109
Wald chi2(3) =
1.11
Log likelihood = -102.3917 Prob > chi2 =
0.7758
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
newproc |
normscapab | 3.722963 3.615273 1.03 0.303 -3.362842
10.80877
e6 | -.2859454 .6005927 -0.48 0.634 -1.463085
.8911945
d12b | -.0033544 .0098363 -0.34 0.733 -.0226332
.0159244
_cons | -.1114548 1.10271 -0.10 0.919 -2.272727
2.049817
-------------+----------------------------------------------------------------
lnsigma2 |
normscapab | 1.320455 1.518283 0.87 0.384 -1.655325
4.296234
------------------------------------------------------------------------------
Likelihood-ratio test of lnsigma2=0: chi2(1) = 0.61 Prob > chi2 =
0.4342
The model now converged but the prob is very high. Should I suppose the
presence of Heteroskedasticity now and before?
Best,
Alejandra
-------------------------- Messaggio originale ---------------------------
Oggetto: Heteroskedastic probit
Da: [email protected]
Data: Gio, 30 Aprile 2009 4:19 pm
A: [email protected]
--------------------------------------------------------------------------
Dear Statalist,
I run this model:
hetprob newprod normscapab n5a e6 d12b Le8c if a4b==5, het(normscapab)
and obtained this result: convergence not achieved.
Is tis anyway to rescue the model?
Best,
Alejandra Molina
Heteroskedastic probit model Number of obs =
165
Zero outcomes =
56
Nonzero outcomes =
109
Wald chi2(5) =
10.61
Log likelihood = -97.15464 Prob > chi2 =
0.0596
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
newproc |
normscapab | -.1146763 .0552553 -2.08 0.038 -.2229748
-.0063779
n5a | 4.15e-10 4.03e-10 1.03 0.303 -3.75e-10
1.20e-09
e6 | .0098537 .0189325 0.52 0.603 -.0272533
.0469607
d12b | -.0004825 .0004277 -1.13 0.259 -.0013208
.0003557
Le8c | 7.37e-07 4.60e-07 1.60 0.110 -1.66e-07
1.64e-06
_cons | .0818565 .0498956 1.64 0.101 -.015937
.1796501
-------------+----------------------------------------------------------------
lnsigma2 |
normscapab | -5.064362 . . . .
.
------------------------------------------------------------------------------
convergence not achieved
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