Parametric Modeling of Quantile Regression Coefficient Functions With Longitudinal Data

Paolo Frumento, Matteo Bottai and Ivan Fernandez-Val

Journal of the American Statistical Association, 2021, vol. 116, issue 534, 783-797

Abstract: In ordinary quantile regression, quantiles of different order are estimated one at a time. An alternative approach, which is referred to as quantile regression coefficients modeling (qrcm), is to model quantile regression coefficients as parametric functions of the order of the quantile. In this article, we describe how the qrcm paradigm can be applied to longitudinal data. We introduce a two-level quantile function, in which two different quantile regression models are used to describe the (conditional) distribution of the within-subject response and that of the individual effects. We propose a novel type of penalized fixed-effects estimator, and discuss its advantages over standard methods based on l1 and l2 penalization. We provide model identifiability conditions, derive asymptotic properties, describe goodness-of-fit measures and model selection criteria, present simulation results, and discuss an application. The proposed method has been implemented in the R package qrcm.

Date: 2021
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Citations: View citations in EconPapers (6)

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DOI: 10.1080/01621459.2021.1892702

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