Hello folks. My apologies if this has been asked before--I did search, but failed to find anything on it.
When I estimate the same model via -glm- and -sem-, I get the same point estimates, but different SEs (see example below). Given that both procedures use MLE, I expected everything (including SEs) to be identical. Was I wrong to expect that? Are there any options with different defaults that I need to tinker with?
Thanks for any insight you can offer.
By the way, I am using v13.1 (for Windows).
Cheers,
Bruce
When I estimate the same model via -glm- and -sem-, I get the same point estimates, but different SEs (see example below). Given that both procedures use MLE, I expected everything (including SEs) to be identical. Was I wrong to expect that? Are there any options with different defaults that I need to tinker with?
Thanks for any insight you can offer.
By the way, I am using v13.1 (for Windows).
Cheers,
Bruce
Code:
. sysuse auto, clear
(1978 Automobile Data)
.
. glm mpg weight foreign // estimate same model via -glm-
Iteration 0: log likelihood = -194.18306
Generalized linear models No. of obs = 74
Optimization : ML Residual df = 71
Scale parameter = 11.60805
Deviance = 824.1717613 (1/df) Deviance = 11.60805
Pearson = 824.1717613 (1/df) Pearson = 11.60805
Variance function: V(u) = 1 [Gaussian]
Link function : g(u) = u [Identity]
AIC = 5.329272
Log likelihood = -194.1830644 BIC = 518.5831
------------------------------------------------------------------------------
| OIM
mpg | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
weight | -.0065879 .0006371 -10.34 0.000 -.0078366 -.0053392
foreign | -1.650029 1.075994 -1.53 0.125 -3.758939 .4588806
_cons | 41.6797 2.165547 19.25 0.000 37.43531 45.9241
------------------------------------------------------------------------------
. sem (mpg <- weight foreign) // estimate same model via -sem-
Endogenous variables
Observed: mpg
Exogenous variables
Observed: weight foreign
Fitting target model:
Iteration 0: log likelihood = -822.2459
Iteration 1: log likelihood = -822.2459
Structural equation model Number of obs = 74
Estimation method = ml
Log likelihood = -822.2459
------------------------------------------------------------------------------
| OIM
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Structural |
mpg <- |
weight | -.0065879 .0006241 -10.56 0.000 -.007811 -.0053647
foreign | -1.650029 1.053958 -1.57 0.117 -3.715748 .4156902
_cons | 41.6797 2.121197 19.65 0.000 37.52223 45.83717
-------------+----------------------------------------------------------------
var(e.mpg)| 11.13746 1.830987 8.06955 15.37173
------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(0) = 0.00, Prob > chi2 = .
.
. // The -glm- and -sem- commands both use MLE; and the
. // defaults for -glm- are normal error distribution with
. // identity link function. So I expected -glm- and -sem-
. // to yield identical results. The point estimates are
. // the same, but the SEs are not.