Conditional Quantile Functions for Zero-Inflated Longitudinal Count Data

Carlos Lamarche, Xuan Shi and Derek S. Young

Econometrics and Statistics, 2024, vol. 31, issue C, 49-65

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.

Keywords: Zero-inflated count data; Quantile models; Subject heterogeneity; Generalized linear mixed models (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:31:y:2024:i:c:p:49-65

DOI: 10.1016/j.ecosta.2021.09.003

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