Re: st: Underdispersion in count data from a survery

[Laurie Molina <molinalaurie@gmail.com>]

Thank you!


On Thu, Oct 7, 2010 at 9:10 PM, Steve Samuels <sjsamuels@gmail.com> wrote:
> Laurie asked:
>
> " What you are telling me is that when you use survey data, the
>> equidispersion assumption plays no role in the calculation of standard
>> errors because the standard errors are calculation using the survey
>> design information?"
>
> That is exactly what I mean.
>
> Steve
>
> On Thu, Oct 7, 2010 at 7:55 PM, Laurie Molina <molinalaurie@gmail.com> wrote:
>> Thank you Steve very much.
>>
>> Concerning your last post, i did used svy: reg
>> Probably i should have mentioned that, thank you for the check on that.
>>
>> Concerning the previous post:
>> In fact my concern is on the standard errors.
>> Following the first link you mentioned me, i have run
>>
>> glm depvar indepvar [iweight=factor] , link(log) family(poisson)
>> suest . , svy
>>
>> What i get is exactly the same (coefficients and p-values -and so
>> standard errors) as when i run:
>> svy: poisson depvar indepvar
>>
>> To my understand, when you run a poisson regression, while the
>> conditional expectation of y given x is correctly specified i.e.
>> E[y|X]= exp(Xbeta), even if the conditional distribution of y is not
>> poisson, you get consistent estimates, but if the equidispersion
>> asumption is not true, you will get wrong standard errors.
>>
>> May be i am not understanding something, so i will like to confirm something:
>> What you are telling me is that when you use survey data, the
>> equidispersion assumption plays no role in the calculation of standard
>> errors because the standard errors are calculation using the survey
>> design information?
>>
>> Thank you very much again!!
>>
>>
>>
>>
>> On Thu, Oct 7, 2010 at 6:15 PM, Steve Samuels <sjsamuels@gmail.com> wrote:
>>> --
>>> You can use the -glm- command in Stata 9 with survey data (link(log)
>>> family(poisson)) and follow it by -suest- to get valid standard
>>> errors. See http://www.stata.com/statalist/archive/2007-06/msg01103.html.
>>>  You could also run -svy reg- on the log of (count +1).
>>> Under-dispersion is a concern for standard errors when your
>>> inferences are likelihood based. The survey-based commands will base
>>> standard errors on the sample design.
>>>
>>> The Poisson or regression  models for predicting or estimating  means
>>> might be useful even if the data are not Poisson distributed. You
>>> still have to check model diagnostics. See:
>>> http://www.stata.com/meeting/dcconf09/dc09_valliant.pdf  for some
>>> ideas.
>>>
>>> Steve
>>>
>>>
>>>
>>> On Thu, Oct 7, 2010 at 5:46 PM, Laurie Molina <molinalaurie@gmail.com> wrote:
>>>> Hello all,
>>>>
>>>> I have count data from a survey which comes from a complex survey
>>>> design (stratification, clusters, two stages, and probabilistic
>>>> design).
>>>>
>>>> The mean is 1.89 and the variance is 1.14.
>>>>
>>>> Fallowing "Essentials of count data regression" by Cameron and Trivedi
>>>> (1999)To test for underdispersion i ran the following auxiliary
>>>> regression:
>>>>
>>>> ((yi - muhati)^2 - yi)/muhati= alpha*muhati + ui
>>>>
>>>> where yi is the i observation of  original dependent count variable,
>>>> muhati is the i observation of the fitted values resulting from the
>>>> poisson coefficients estimated, ui is the error term, and the
>>>> objective is to test alpha= 0 Vs alpha<0.
>>>>
>>>> Whith a p-value of 1, the hypothesis of alpha < 0 cannot be rejected.
>>>>
>>>> So my question is... What now? My data is underdispersed so the
>>>> standard errors estimated when i run the Poisson regression are not
>>>> correct (even when the estimates of my coefficents are consistent).
>>>>
>>>> Is there any solution for this problem in Stata 9, that can be used
>>>> with survey data?
>>>>
>>>> Thank you all very much in advance.
>>>>
>>>> Laurie
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>>>>
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