At 10:27 AM 4/30/2009, [email protected] wrote:
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?
I would suppose just the opposite. Neither the z value in the
lnsigma2 equation nor the corresponding LR test indicate that there
is a problem with hetero, at least with normscapab. Indeed, having
the hetero equation may be keeping you from finding significant
results in the choice equation. It looks to me like you should just
do a regular probit model.
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME: (574)289-5227
EMAIL: [email protected]
WWW: http://www.nd.edu/~rwilliam
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
