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A Generalized Parametric Selection Model for Non-Normal Data

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  • James E. Prieger

    (Department of Economics, University of California Davis)

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.

Suggested Citation

  • James E. Prieger, 2003. "A Generalized Parametric Selection Model for Non-Normal Data," Working Papers 22, University of California, Davis, Department of Economics.
    • Handle: RePEc:cda:wpaper:22
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    References listed on IDEAS

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