st: Zero-inflated Negative Binomial models for Panel data

["Pavlos C. Symeou" <p.symeou@lmu.de>]

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.
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.
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

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?
Namely, the ml estimator would look like this:
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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