# Zero-inflated Negative Binomial models for Panel data

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## Zero-inflated Negative Binomial models for Panel data

 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 * *   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/