A Generalized Parametric Selection Model for Non-Normal Data

James E. Prieger ()

Department of Economics from California Davis - Department of Economics

Abstract: I develop a new approach for sample selection problems that allows parametric forms of any type to be chosen for both for the selection and the observed variables. The Generalized Parametric Selection (GPS) model can incorporate both duration and count data models, unlike previous parametric models. MLE does not require numerical integration or simulation techniques, unlike previous models for count data. I discuss application to common duration models (exponential, Weibull, log-logistic) and count models (Poisson, negative binomial). I demonstrate the usefulness of the model with an application to the effects of insurance status and managed care on hospitalization duration data. The example indicates that the GPS model may be preferred even in cases for which other parametric approaches are available.

New Economics Papers: this item is included in nep-ecm
View list of references

Downloads: (external link)
http://www.econ.ucdavis.edu/working_papers/00-9.pdf (application/pdf)

Related works:
Working Paper: A Generalized Parametric Selection Model for Non-normal Data (2000)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:fth:caldec:00-09

Access Statistics for this paper

More papers in Department of Economics from California Davis - Department of Economics
Address: University of California Davis - Department of Economics. One Shields Ave., California 95616-8578
Contact information at EDIRC.
Series data maintained by Thomas Krichel ().