Semiparametric Count Data Modeling with an Application to Health Service Demand

P. Bach, Helmut Farbmacher and M. Spindler

Health, Econometrics and Data Group (HEDG) Working Papers from HEDG, c/o Department of Economics, University of York

Abstract: Heterogeneous effects are prevalent in many economic settings. As the functional form between outcomes and regressors is often unknown apriori, we propose a semiparametric negative binomial count data model based on the local likelihood approach and generalized product kernels, and apply the estimator to model demand for health care. The local likelihood framework allows us to leave the functional form of the conditional mean unspecified while still exploiting basic assumptions in the count data literature (e.g., non-negativity). The generalized product kernels allow us to simultaneously model discrete and continuous regressors, which reduces the curse of dimensionality and increases its applicability as many regressors in the demand model for health care are discrete.

Keywords: semiparametric; nonparametric; count data; health care demand (search for similar items in EconPapers)
JEL-codes: C14 C25 I10 (search for similar items in EconPapers)
Date: 2016-08
New Economics Papers: this item is included in nep-ecm and nep-hea
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Semiparametric count data modeling with an application to health service demand (2018)
Working Paper: Semiparametric count data modeling with an application to health service demand (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:yor:hectdg:16/20

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