A bias-adjusted estimator in quantile regression for clustered data

Maria Laura Battagliola, Helle Sørensen, Anders Tolver and Ana-Maria Staicu

Econometrics and Statistics, 2022, vol. 23, issue C, 165-186

Abstract: Quantile regression models with random effects are useful for studying associations between covariates and quantiles of the response distribution for clustered data. Parameter estimation is examined for a class of mixed-effects quantile regression models, with focus on settings with many but small clusters. The main contributions are the following: (i) documenting that existing methods may lead to severely biased estimators for fixed effects parameters; (ii) proposing a new two-step estimation methodology where predictions of the random effects are first computed by a pseudo likelihood approach (the LQMM method) and then used as offsets in standard quantile regression; (iii) proposing a novel bootstrap sampling procedure in order to reduce bias of the two-step estimator and compute confidence intervals. The proposed estimation and associated inference is assessed numerically through rigorous simulation studies and applied to an AIDS Clinical Trial Group (ACTG) study.

Keywords: Linear quantile regression; Clustered data; Random effects; Bias-adjustment; Wild bootstrap; AIDS clinical trial group study (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:23:y:2022:i:c:p:165-186

DOI: 10.1016/j.ecosta.2021.07.003

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