A generalized parametric selection model for non-normal data

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 00-9

Classification and Topics

• Classification: Wirtschaft
• Subject: sample selection ; bivariate distribution ; duration models ; count data models ; lee's model ; managed care ; Medical Expenditure Panel Survey ; Statistisches Auswahlverfahren

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Prieger, James E.
• Event: Veröffentlichung
• (who): University of California, Department of Economics
• (where): Davis, CA
• (when): 2000

Further information

• Handle: http://hdl.handle.net/10419/79431
• Last update: 10.03.2025, 11:45 AM CET