I am trying to estimate a model predicting "food security" (a construct
ranging roughly from adequate food levels with no insecurity to severe
hunger). Food security is a latent variable, where I observe whether or not
a household experienced each of 18 food-related problems. The problems
generally are of increasing severity (decreasing prevalence). A sizable
majority of households experience none of the 18 problems.
I have so far estimated OLS and ordered probit models, which I realize are
not ideal. I have also estimated a probit model predicting whether a
household experiences 0 or more than 0 problems along with an ordered probit
model for those households experiencing at least one problem. Have been
told to consider a negative binomial or zero-inflated poisson regression
model, but these don't seem quite right (given that I'm not looking at
counts of independent events). Seems like I'd want a model similar to an
ordered probit but with an assumed latent distribution that was non-normal.
Any suggestions are appreciated. - Scott
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Scott Winship
Ph.D. Candidate in
Sociology & Social Policy
Harvard University
[email protected]
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