panels. My panel sizes were very large (2000 +), but I don't really
understand how panels could be completely determined in the same way
an individual observation can be in logit. Moreover, I get no such
error fitting the same random effects model in other programs,
(gllamm, or Proc NLMIXED in SAS, ) (see results of the two models for
xtlogit and gllamm below). Thanks very much for any insight!
. xtlogit hcgi m_l10count8 if anycontact==1, i(beachnum)
Fitting comparison model:
Iteration 0: log likelihood = -4026.8522
Iteration 1: log likelihood = -4020.8548
Iteration 2: log likelihood = -4020.8325
Iteration 3: log likelihood = -4020.8325
Fitting full model:
tau = 0.0 log likelihood = -2562.2351
tau = 0.1 log likelihood = -2556.2705
tau = 0.2 log likelihood = -2558.3731
Iteration 0: log likelihood = -2556.2705
Iteration 1: log likelihood = -2555.8389
Iteration 2: log likelihood = -2555.2593
Iteration 3: log likelihood = -2555.2165
Iteration 4: log likelihood = -2555.2117
Iteration 5: log likelihood = -2555.2117
Random-effects logistic regression Number of obs = 13927
Group variable (i): beachnum Number of groups = 4
Random effects u_i ~ Gaussian Obs per group: min = 1305
avg = 3481.8
max = 7502
Wald chi2(1) = 3.38
Log likelihood = -2555.2117 Prob > chi2 = 0.0659
------------------------------------------------------------------------------
hcgi | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
m_l10count8 | .1326954 .0721532 1.84 0.066 -.0087223 .2741131
_cons | -2.628946 .2267516 -11.59 0.000 -3.073371 -2.184521
-------------+----------------------------------------------------------------
/lnsig2u | -2.390896 .9681572 -4.288449 -.4933428
-------------+----------------------------------------------------------------
sigma_u | .3025684 .1464669 .1171588 .7813974
rho | .0270738 .025502 .0041549 .1565414
------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) = 2931.24 Prob >= chibar2 = 0.000
Note: 2 completely determined panels
. gllamm hcgi m_l10count8 if anycontact==1, i(beachnum)
family(binomial) link(logit)
number of level 1 units = 13927
number of level 2 units = 4
Condition Number = 5.8527904
gllamm model
log likelihood = -4012.7333
------------------------------------------------------------------------------
hcgi | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
m_l10count8 | .1129098 .0586056 1.93 0.054 -.001955 .2277747
_cons | -2.566529 .1207458 -21.26 0.000 -2.803186 -2.329872
------------------------------------------------------------------------------
Variances and covariances of random effects
-----------------------------------------------------------------------------
***level 2 (beachnum)
var(1): .04480799 (.018906)
-----------------------------------------------------------------------------
On Wed, 9 Mar 2005 16:15:00 -0600, Gustavo Sanchez <[email protected]> wrote:
> Maria [[email protected]] asked:
>
> >I've run a random effects model using xtlogit (attaching output below) and
> I
> >have two questions:
>
> >1) what does the note at the end of the output "77 completely determined
> >panels" means?
> >2) How are sigma_u and rho estimated and what could be the reasons to not
> >get an estimate?
>
> >I can give more details about what I'm trying to do if necessary.
>
> >Does anyone can help me?
>
> >Thanks, Maria
>
> Please, look at the FAQ for completely determined observations for -logit-
> (it also applies to completely determined panels for -xtlogit-):
>
> http://www.stata.com/support/faqs/stat/logitcd.html
>
> Regarding rho and sigma:
>
> - rho is obtained as: rho = (sigma_u)^2 / ((sigma_u)^2 + (sigma_e)^2)
>
> Where sigma_u and sigma_e correspond to the unobserved individual
> specific component and the idiosyncratic component of the error term:
>
> error = u_i + e_i_t
>
> (See page 135 of the cross-sectional time-series manual for details)
>
> - sigma_u, is the standard deviation of the random effect term, which
> measures the degree of heterogeneity in u_i
>
> Sincerely,
>
> Gustavo
> ([email protected])
>
> *
> * For searches and help try:
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> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
*
* For searches and help try:
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