Likelihood-Based Approaches to Modeling Demand for Medical Care

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/10261/1912

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DC Field	Value	Language
dc.creator	Creel, Michael	-
dc.creator	Farell, Montserrat	-
dc.date	2007-11-06T10:51:51Z	-
dc.date	2007-11-06T10:51:51Z	-
dc.date	2001-10-05	-
dc.date.accessioned	2017-01-31T00:58:10Z	-
dc.date.available	2017-01-31T00:58:10Z	-
dc.identifier	http://hdl.handle.net/10261/1912	-
dc.identifier.uri	http://dspace.mediu.edu.my:8181/xmlui/handle/10261/1912	-
dc.description	We review recent likelihood-based approaches to modeling demand for medical care. A semi-nonparametric model along the lines of Cameron and Johansson's Poisson polynomial model, but using a negative binomial baseline model, is introduced. We apply these models, as well a semiparametric Poisson, hurdle semiparametric Poisson, and finite mixtures of negative binomial models to six measures of health care usage taken from the Medical Expenditure Panel survey. We conclude that most of the models lead to statistically similar results, both in terms of information criteria and conditional and unconditional prediction. This suggests that applied researchers may not need to be overly concerned with the choice of which of these models they use to analyze data on health care demand.	-
dc.language	eng	-
dc.relation	UFAE and IAE Working Papers	-
dc.relation	498.01	-
dc.rights	openAccess	-
dc.subject	Health care demand	-
dc.subject	Count data	-
dc.subject	Maximum likelihood	-
dc.title	Likelihood-Based Approaches to Modeling Demand for Medical Care	-
dc.type	Documento de trabajo	-
Appears in Collections:	Digital Csic

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