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Conditional Quantile Functions for Zero-Inflated Longitudinal Count Data

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  • Lamarche, Carlos
  • Shi, Xuan
  • Young, Derek S.

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.

Suggested Citation

  • Lamarche, Carlos & Shi, Xuan & Young, Derek S., 2024. "Conditional Quantile Functions for Zero-Inflated Longitudinal Count Data," Econometrics and Statistics, Elsevier, vol. 31(C), pages 49-65.
    • Handle: RePEc:eee:ecosta:v:31:y:2024:i:c:p:49-65
    DOI: 10.1016/j.ecosta.2021.09.003
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