01.01. - 22.08.2017 / Health Econometrics
Semiparametric Count Data Modeling with an Application to Health Service Demand
Heterogeneous effects are prevalent in many economic settings. As the functional form between outcomes and regressors is often unknown a-priori, 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 exploting basic assumptions in the count data literature (e.g., non-negativity). The generalized product kernels allows us to simultaneously model discrete and continuous regressors, which reduces the curse of dimensionality and increases its applicability as many regressors in the demand for health care are discrete.