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Re: st: Regression Equation for Zero inflated negative binomial


From   Muhammad Anees <anees@aneconomist.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Regression Equation for Zero inflated negative binomial
Date   Mon, 19 Sep 2011 14:16:25 +0500

You could check the model formulas from
http://support.sas.com/documentation/cdl/en/etsug/60372/HTML/default/viewer.htm#etsug_countreg_sect019.htm.
Anyway I am not sure if you would find any straight forward tool to
convert the estimation results into equation form. All you need to do
is manually. I think you do not need to show it in equation form. All
you need to do is to write the results in a tabular form with
explanations in mathematical form. This can be done from any good book
or joutnal article. Hope this helps.

2011/9/19 rachel grant <rachelannegrant@gmail.com>:
> Thank you for your help. Maybe I need to explain the problem more clearly
> I have used Zero Inflated negative binomial regression in Stata to
> model some overdispersed, zero inflated count data. I got very nice
> results and also used the postestimation tools to predict Y values.
> This is included in my PhD thesis which I am about to submit. My PhD
> supervisor said as well as presenting the results (i.e cofficients, p
> values etc.) I also have to show "the model". I think by this he means
> the regression equation in the form:
>
> natural log (Y) = B0+B1X1 + B2X2..............
>
> I cannot find out what the equation is for ZINB models and also I
> cannot find out how to make Stata display this model.
> I could just add in the coefficients myself BUT I am not sure of the
> exact formula of the model for ZINB (especially the ZI part) as I
> think it may be more complicated than the simple Poisson
>
> log e  (Y) = β0 + β1Χ1 + β2Χ2 ...
>
> I have searched everywhere to find a general equation for ZINB with no
> luck and also read all the Stata help files.
> I am very confused about why this seemingly simple thing should prove
> so impossible! I am a beginner with stata, and previously only used
> simple linear regression so thank you for your patience.
>
> --
> regards, Rachel Grant
>
> *
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> *   http://www.ats.ucla.edu/stat/stata/
>



-- 


Regards

Anees

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