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Re: st: xtnbreg, nbreg, and tests of assumptions


From   "Mary E. Mackesy-Amiti" <mmamiti@uic.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: xtnbreg, nbreg, and tests of assumptions
Date   Thu, 16 Dec 2010 11:19:29 -0600

As Maarten said, xtnbreg shows you the relationship between type of hospital and days of training within groups (fi). To answer your original question "how should I decide which one I should be using" the likelihood ratio test at the bottom indicates that the panel estimator is significantly different from the pooled estimator. The question is do you want within-group effects or average effects? If you do want average effects, you may want to use -xtnbreg, pa vce(robust)- rather than -nbreg, vce(cluster fi)-. The pa model assumes an exchangeable correlation structure which may be more appropriate.


On 12/15/2010 11:12 AM, Dalhia wrote:
oops sorry. don't know what I was thinking. Thanks Mary for the correction.

Here are the results for xtnbreg that don't make sense. Basically, I have panel data on hospitals (private, public, and associates), and looking at the averages of the number of training days for each hospital type, 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).

When I run it using nbreg I get the opposite result (the result I was expecting - public and associates are have greater rate of training per year compared to private).

Thanks for your help.
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

.



--
Mary Ellen Mackesy-Amiti, Ph.D.
Research Assistant Professor
Community Outreach Intervention Projects (COIP)
School of Public Health m/c 923
Division of Epidemiology and Biostatistics
University of Illinois at Chicago
ph. 312-355-4892
fax: 312-996-1450

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