RE: st: Zero Inflated Poisson Regression

["Scott Holupka" <Scott.Holupka@jhu.edu>]

Thanks for the suggestions.  We've tried in the past to find an appropriate
IV, but so far haven't found anything that works.  At least with propensity
we can try to control for any observed differences.  

Scott


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Cameron McIntosh
Sent: Monday, August 06, 2012 3:10 PM
To: STATA LIST
Subject: RE: st: Zero Inflated Poisson Regression

Scott,

Good question. Generally, I don't know if there is very much out there on
how to fit ZIPs, or count/rate variable regression models in general, with
non-linear relations (e.g., quadratic as you seem to suggest).  I don't know
what Stata has to offer in this regard (as I'm not a "Stata guy"), but I
might suggest a neural network approach, perhaps using MATLAB:

Nader, F., Hong, G., Kazem, M., Ali, S.S., Keramat, N., & Reza, E.M. (2009).
Nonlinear Poisson regression using neural networks: a simulation study.
Neural Computing & Applications, 18(8), 939-943.
http://www.mscs.dal.ca/~hgu/Neural%20Comput%20&%20Applic.pdf

As for your endogeneity problem, MATLAB also does propensity score matching,
and you may also want to consider using instrumental variables, if you can
find some good ones in your data set.

Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the
implementation of propensity score matching. Journal of Economic Surveys,
22, 31-72.

Stuart, E. A. (2010). Matching methods for causal inference: A review and a
look forward. Statistical Science, 25, 1-21.

Bollen, K.A. (2012). Instrumental Variables in Sociology and the Social
Sciences. Annual Review of Sociology, 38, 37-72.
http://www.annualreviews.org/doi/abs/10.1146/annurev-soc-081309-150141?journ
alCode=soc

Perhaps some more experienced Stata programmers could provide you with a
Stata solution, however. Anyway, hope this helps.

Cam

----------------------------------------
> From: Scott.Holupka@jhu.edu
> To: statalist@hsphsun..harvard.edu
> Subject: st: Zero Inflated Poisson Regression
> Date: Mon, Aug 012 3::8::2 -400<
>
> This is mainly a question about running a zero-inflated poisson regression
> using zip (Stata 0..)), but it's also a more general question of whether
> Statalisters think I'm using the procedures in an appropriate way.
>
> My analysis is examining several expenditure categories. Typical of
> expenditure data, the outcome variables are all skewed. Also typical is
> that several outcomes have a large percentage (0%% to 0%%) of cases
> reporting zero. I am therefore considering using zero-inflated poisson
> models - zip - to examine these outcomes.
>
> Prior research also suggests that the relationship between our primary
> independent variable - call it H - and expenditures will not be linear. In
> particular, we expect spending may be lower at both high and low values of
> H. I have previously used polynominal models to examine this relationship,
> but I'm not sure if polynomials can be used with negative poissson models.
> I am therefore also considering using a piecewise regression approach with
> ZIP.
>
> Finally, I'm concerned about omitted variable bias since I don't have a
> randomized sample. Again, in previous work I've used propensity score
> methods to account for differences in observed characteristics.
>
> I know how to implement each of these methods in Stata, but I'm wondering
if
> it's appropriate to use all three methods at once. My current plan is to
> run propensity analyses to identify similar groups based on observed
> characteristics, then use those groups as covariates in a zero-inflated
> poisson model that also include polynomial terms of H (e.g. H and
> H-squared), or computing piecewise dummy variables of H.
>
> Any thoughts on whether this approach seems appropriate, particularly
> whether ZIP can handle both the propensity covariates and polynomial
terms,
> would be appreciated.
>
> Thanks,
>
> Scott
>
>
>
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