st: xtlogit and number of observations

[Traci A Schlesinger <tracias@Princeton.EDU>]

Fellow Statalisters:

i tried to post this several hours ago, but it still hasn't come up, so 
i've decided to try again.

I am using xtlogit to analyze pretrial release decisions, such as 
whether someone is denied bail or given a nonfinancial release.  I have 
5 years of individual level data which includes all individuals arrested 
for felonies in 54 counties.  I am using a county level fixed effect.  
when i aggregate all 5 years together, the regression results that are 
returned to me are filled with dots where there should be numbers. 
however, when i run the regressions by year, i get normal results. or, 
if i run all 5 years together but by race group or crime type, i get 
normal results.  In fact, it seems if i partition the data in any way 
that brings the number of observations below 40,000, i get results but 
if the number of observations is above 40,000, i get dots.  Of course, 
other commands can handle more observations than this --  for instance, 
if i use xtreg to run by equation with all years, all race groups, and 
all crime types, i get sensible results.  Does xtlogit have a lower 
limit to the number of observations it can handle?
if anyone could help be figure out what is going on -- or even better, 
how to fix it -- please let me know!
i've pasted the relevant parts from my log file below.
thanks!!!
traci

> xtlogit dbail black latino age agesq pripris prijail prifconv 
privconv pridco
> nv cjstatus priorfta chg1att totchgs1 murder rape robbery assault 
oviolent bu
> rglary theft oproperty drugt odrug weapons driving, fe i(county) or;

note: multiple positive outcomes within groups encountered.
note: 2 groups (451 obs) dropped due to all positive or
     all negative outcomes.
Iteration 0:   log likelihood = -8.988e+307

Conditional fixed-effects logit                 Number of obs      =     
40593
Group variable (i) : county                     Number of groups   
=        52

                                               Obs per group: min 
=       109
                                                              avg =     
780.6
                                                              max 
=      4992

                                               LR chi2(25)        
=         .
Log likelihood  =          .                    Prob > chi2        
=         .

------------------------------------------------------------------------------
      dbail |         OR   Std. Err.      z    P>|z|     [95% Conf. 
Interval]
-------------+----------------------------------------------------------------
      black |                     .        .       .            
.           .
     latino |                     .        .       .            
.           .
        age |                     .        .       .            
.           .
      agesq |                     .        .       .            
.           .
    pripris |                     .        .       .            
.           .
    prijail |                     .        .       .            
.           .
   prifconv |                     .        .       .            
.           .
   privconv |                     .        .       .            
.           .
   pridconv |                     .        .       .            
.           .
   cjstatus |                     .        .       .            
.           .
   priorfta |                     .        .       .            
.           .
    chg1att |                     .        .       .            
.           .
   totchgs1 |                     .        .       .            
.           .
     murder |                     .        .       .            
.           .
       rape |                     .        .       .            
.           .
    robbery |                     .        .       .            
.           .
    assault |                     .        .       .            
.           .
   oviolent |                     .        .       .            
.           .
   burglary |                     .        .       .            
.           .
      theft |                     .        .       .            
.           .
  oproperty |                     .        .       .            
.           .
      drugt |                     .        .       .            
.           .
      odrug |                     .        .       .            
.           .
    weapons |                     .        .       .            
.           .
    driving |                     .        .       .            
.           .
------------------------------------------------------------------------------

. xtlogit dbail black latino age agesq pripris prijail prifconv privconv 
pridco
> nv cjstatus priorfta chg1att totchgs1 murder rape robbery assault 
oviolent bu
> rglary theft oproperty drugt odrug weapons driving if year == 1990, 
fe i(coun
> ty) or;

note: multiple positive outcomes within groups encountered.
note: 10 groups (514 obs) dropped due to all positive or
     all negative outcomes.
Iteration 0:   log likelihood = -1025.0744
Iteration 1:   log likelihood = -850.28866
Iteration 2:   log likelihood = -845.87709
Iteration 3:   log likelihood = -845.86588
Iteration 4:   log likelihood = -845.86588

Conditional fixed-effects logit                 Number of obs      
=      4163
Group variable (i) : county                     Number of groups   
=        28

                                               Obs per group: min 
=        41
                                                              avg =     
148.7
                                                              max 
=       690

                                               LR chi2(25)        =    
420.16
Log likelihood  = -845.86588                    Prob > chi2        =    
0.0000

------------------------------------------------------------------------------
      dbail |         OR   Std. Err.      z    P>|z|     [95% Conf. 
Interval]
-------------+----------------------------------------------------------------
      black |   1.395709   .2225102     2.09   0.037     1.021157    
1.907643
     latino |   .9708441   .1930946    -0.15   0.882     .6574323    
1.433666
        age |   1.088391   .0428947     2.15   0.032     1.007484    
1.175795
      agesq |   .9988118    .000582    -2.04   0.041     .9976716    
.9999532
    pripris |   1.314163   .0872487     4.12   0.000     1.153817    
1.496791
    prijail |   .9792627   .0270809    -0.76   0.449     .9275979    
1.033805
   prifconv |   1.032692   .0390599     0.85   0.395     .9589049    
1.112157
   privconv |   1.003938   .0703026     0.06   0.955     .8751852    
1.151632
   pridconv |   .9611685   .0638591    -0.60   0.551     .8438136    
1.094845
   cjstatus |    4.67771   .7036319    10.26   0.000     3.483322    
6.281639
   priorfta |   1.460088   .2553298     2.16   0.030     1.036399    
2.056985
    chg1att |   1.968277   .5003353     2.66   0.008     1.195945    
3.239376
   totchgs1 |   1.062049   .0453806     1.41   0.159     .9767272    
1.154824
     murder |   18.71818   9.604054     5.71   0.000     6.847358    
51.16866
       rape |   1.443282   .7784431     0.68   0.496     .5014762    
4.153862
    robbery |   2.036161   .8609694     1.68   0.093     .8889769    
4.663734
    assault |   .9645549   .4031453    -0.09   0.931     .4251662    
2.188241
   oviolent |   .5543121   .3317127    -0.99   0.324     .1715443    
1.791151
   burglary |   .8193079   .3439418    -0.47   0.635     .3598458    
1.865425
      theft |   .8282786    .343824    -0.45   0.650     .3671445    
1.868598
  oproperty |   .4920875   .2233401    -1.56   0.118     .2021686    
1.197763
      drugt |   .7131889   .2894573    -0.83   0.405     .3219089    
1.580069
      odrug |   .5001843   .2156836    -1.61   0.108      .214825    
1.164596
    weapons |   .3495524   .2299102    -1.60   0.110     .0963061    
1.268735
    driving |   .5510242   .3453503    -0.95   0.342     .1613193    
1.882153
------------------------------------------------------------------------------



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