Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

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 |

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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: xtnbreg, nbreg, and tests of assumptions***From:*Dalhia <ggs_da@yahoo.com>

- Prev by Date:
**RE: RE: st: Converting a SAS datastep to Stata** - Next by Date:
**st: RE: 2nd Step GMM estimation with nonlinear endogenous regressors is biased?** - Previous by thread:
**Re: st: xtnbreg, nbreg, and tests of assumptions** - Next by thread:
**st: how to fill some gross wage missings if I have the net wage** - Index(es):