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From |
sjsamuels@gmail.com |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Query about negative binomial and poisson with an without survey weighted data |

Date |
Wed, 22 Jul 2009 17:24:15 -0400 |

-- The difference in behavior between the non-survey and survey results is due to the difference between likelihood-based and design-based inference. The MLE for the constant coefficient is the log of the mean, and for both non-survey models, the MLE of the mean is the sample mean. Both non-survey programs compute the standard error from the likelihood information matrix. For -poisson- the variance of an observation is equal to the man, and the standard error will be the square root of the mean divided by n. You can see this if you generate your results in terms of the mean, not the log of the mean. Generate a variable dummy equal to 1, then run: sum drinkdays poisson drinkdays dummy, irr nocons In the negative binomial model, the variance of the an individual observation is larger than that in the Poisson model. Therefore, the standard error of the estimated mean (or log mean), computed from the likelihood information matrix, will also be larger, as you observed. Stata's survey programs substitute weighted sums for plain sums in the likelihood equations, and then solve the weighted equations. Because the MLE is the mean, -svy: poisson- and -svy: nbreg- both estimate the model mean as the weighted sample mean. Because the standard error computations are "design-based" and model free (e.g. think "jackknife"), each program estimates the same "robust" standard error. -Steve * * 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**:**st: Query about negative binomial and poisson with an without survey weighted data***From:*"Jason Ferris" <JasonF@TURNINGPOINT.ORG.AU>

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