st: Variance components model: why is the level two variance so high

[liliana <liliana.andriano@gmail.com>]

Hello, 

I am estimating a variance components model with xtmelogit because my
independent variable is a dummy. However, I get no odds ratio for the
constant and a really high (meaningless) level 2 variance. Do you know why?
With the estimates table command I get the odds ratio for the intercept,
which is tremendously high: 4456.12.
These are the command and the table stata gives me back. I have added the
laplace option because, otherwise, Stata would have been really slow. 



. xtmelogit y || cluster:, laplace variance or

Refining starting values: 

Iteration 0:   log likelihood = -1069.1034  
Iteration 1:   log likelihood = -1046.5944  
Iteration 2:   log likelihood = -1045.6258  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -1045.6258  
Iteration 1:   log likelihood = -1016.1863  
Iteration 2:   log likelihood = -993.04989  (not concave)
Iteration 3:   log likelihood = -989.44747  
Iteration 4:   log likelihood = -980.82198  
Iteration 5:   log likelihood = -980.77838  
Iteration 6:   log likelihood = -980.77835  

Mixed-effects logistic regression               Number of obs      =    
12451
Group variable: v001                            Number of groups   =     
3252

                                                Obs per group: min =        
2
                                                               avg =      
3.8
                                                               max =       
17

Integration points =   1                        Wald chi2(0)       =        
.
Log likelihood = -980.77835                     Prob > chi2        =        
.

------------------------------------------------------------------------------
       b5_60 | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+----------------------------------------------------------------
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf.
Interval]
-----------------------------+------------------------------------------------
v001: Identity               |
                  var(_cons) |   31.74218   5.709468      22.31162    
45.1588
------------------------------------------------------------------------------
LR test vs. logistic regression: chibar2(01) =   169.51 Prob>=chibar2 =
0.0000

Note: log-likelihood calculations are based on the Laplacian approximation.



Any help would be really appreciated. 

Thanks,
Liliana 




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