st: strange coefficients in xtnbreg

[Dalhia <ggs_da@yahoo.com>]

Hi, 

I have panel data on hospitals (private, public, and associates), and looking at the averages of the number of training days that each hospital type underwent, I can see that private hospitals have lower number of training days compared to public hospitals. Associate hospitals fall in the mid-range. However, when I run this model using xtnbreg (with random effects), I get a funny result. It looks like public and associates have lower rate of training days in a year compared to private. Am I interpreting the coefficients wrong or is there something else going on? (output attached below).

Thanks 
dalhia


 xtnbreg train asso pub if train<12000, re irr
note: you are responsible for interpretation of non-count dep. variable

Fitting negative binomial (constant dispersion) model:

Iteration 0:   log likelihood = -1341968.9  
Iteration 1:   log likelihood = -1341967.5  
Iteration 2:   log likelihood = -1341967.5  

Iteration 0:   log likelihood = -504693.72  
Iteration 1:   log likelihood = -35614.007  
Iteration 2:   log likelihood =  -35604.55  
Iteration 3:   log likelihood = -35604.545  
Iteration 4:   log likelihood = -35604.545  

Iteration 0:   log likelihood = -35604.545  
Iteration 1:   log likelihood = -35595.175  
Iteration 2:   log likelihood = -35595.145  
Iteration 3:   log likelihood = -35595.145  

Fitting full model:

Iteration 0:   log likelihood = -81145.913  
Iteration 1:   log likelihood = -49940.372  (not concave)
Iteration 2:   log likelihood = -42786.562  (not concave)
Iteration 3:   log likelihood = -35793.307  
Iteration 4:   log likelihood =  -33256.88  
Iteration 5:   log likelihood = -33190.785  
Iteration 6:   log likelihood = -33150.666  
Iteration 7:   log likelihood = -33150.622  
Iteration 8:   log likelihood = -33150.622  

Random-effects negative binomial regression     Number of obs      =      7522
Group variable: fi                              Number of groups   =      1873

Random effects u_i ~ Beta                       Obs per group: min =         1
                                                               avg =       4.0
                                                               max =         5

                                                Wald chi2(2)       =      7.29
Log likelihood  = -33150.622                    Prob > chi2        =    0.0261

------------------------------------------------------------------------------
       train |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        asso |   .8803461   .0551126    -2.04   0.042     .7786914    .9952712
         pub |   .9029852   .0380889    -2.42   0.016     .8313349    .9808108
-------------+----------------------------------------------------------------
       /ln_r |  -.8268984   .0334362                     -.8924322   -.7613647
       /ln_s |   .7346747   .0714634                      .5946091    .8747404
-------------+----------------------------------------------------------------
           r |   .4374038   .0146251                      .4096582    .4670286
           s |   2.084804   .1489872                      1.812322    2.398253
------------------------------------------------------------------------------
Likelihood-ratio test vs. pooled: chibar2(01) =  4889.04 Prob>=chibar2 = 0.000



      
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