Conditional Quantile Functions for Zero-Inflated Longitudinal Count Data

Abstract

The identification and estimation of conditional quantile functions for count responses using longitudinal data are considered. The approach is based on a continuous approximation to distribution functions for count responses within a class of parametric models that are commonly employed. It is first shown that conditional quantile functions for count responses are identified in zero-inflated models with subject heterogeneity. Then, a simple three-step approach is developed to estimate the effects of covariates on the quantiles of the response variable. A simulation study is presented to show the small sample performance of the estimator. Finally, the advantages of the proposed estimator in relation to some existing methods is illustrated by estimating a model of annual visits to physicians using data from a health insurance experiment.