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# st: Zero-inflated Negative Binomial models for Panel data

 From "Pavlos C. Symeou" To statalist@hsphsun2.harvard.edu Subject st: Zero-inflated Negative Binomial models for Panel data Date Tue, 13 Jul 2010 17:42:54 +0200

this is a response to a thread started a couple of months ago about possible ways to estimate Zero-inflated Negative Binomial/Poisson models for Panel data. I am interested in modeling differently the zero-one distribution and the count (non-zero) distribution in my data since 2/3 of my dependent variable's values are zero throughout the time-span of the dataset. The count variable ranges from 0-5.
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I first followed the suggestion made in the thread to look at the paper "From the help desk: hurdle models" by Allen McDowell, published in The Stata Journal (2003) 3, Number 2, pp. 178–184. What the paper illustrates is how to fit a hurdle model using ml’s cluster(), options.
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The commands are the following:

program hurdle_ll
version 8
args lnf beta1 beta2
tempvar pi lambda
quietly generate double ‘pi’ = exp(‘beta1’)
quietly generate double ‘lambda’ = exp(‘beta2’)
quietly replace ‘lnf’ = cond(\$ML_y1==0,-‘pi’, ///
log(1-exp(-‘pi’)) + \$ML_y1*‘beta2’ - ///
log(exp(‘lambda’)-1) - lngamma(\$ML_y1+1))
end

You can then invoke the ml estimator with the commands:
ml model lf hurdle_ll (y = x1 x2) (x1 x2)
ml max, nolog

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My question is the following: can I suggest that I am estimating or approach an estimation of a panel data respective model if I cluster based on each observation's identity (id) and introduce year dummies as regressors?
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Namely, the ml estimator would look like this:
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xi: ml model lf hurdle_ll (y = x1 x2 i.year) (x1 x2 i.year), cluster(id)
```ml max, nolog

I look forward to receiving your insights.

Best,

Pavlos

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