st: decrease ll nested models sem

[Volker Lang <v.lang@uni-tuebingen.de>]

Dear Statalist,

I'm fitting a log-linear regression model using "sem".
(It shall later be extended to a path model with robust s.e.'s,
therefore "sem".)

Question:
Why does the log likelihood in comparison of the two nested models
estimated with "sem" decrease?
(It does not happen if I use a different estimation command, e.g.,
"sureg .. , isure".
Also, in this case coefficients and s.e.'s estimated by "sem" and
"sureg .. , isure" are identical,
so why does the log likelihood differ between these estimation
commands at all?)

Here is the output:

. sem (grade_l <- infov_l)
..
Iteration 1:   log likelihood =  -175.8425

Structural equation model                       Number of obs = 1281
Estimation method  = ml
Log likelihood     =  -175.8425
..

. sem (grade_l <- infov_l ued_l)
..
Iteration 1:   log likelihood =  -513.0155

Structural equation model                       Number of obs = 1281
Estimation method  = ml
Log likelihood     =  -513.0155
..

. sureg (grade_l infov_l), isure
Iteration 1:   tolerance =  7.234e-17
..
. di e(ll)
-677.17747

. sureg (grade_l infov_l ued_l), isure
Iteration 1:   tolerance =  1.447e-16
..
. di e(ll)
-656.64981

Thanks for any hints.

Best regards,
Volker Lang

--
University of Tuebingen
Faculty of Economics and Social Sciences
Department of Sociology
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