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Conditional empirical likelihood for quantile regression models

Author

Listed:
  • Wu Wang

    (Fudan University)

  • Zhongyi Zhu

    (Fudan University)

Abstract

In this paper, we propose a new Bayesian quantile regression estimator using conditional empirical likelihood as the working likelihood function. We show that the proposed estimator is asymptotically efficient and the confidence interval constructed is asymptotically valid. Our estimator has low computation cost since the posterior distribution function has explicit form. The finite sample performance of the proposed estimator is evaluated through Monte Carlo studies.

Suggested Citation

  • Wu Wang & Zhongyi Zhu, 2017. "Conditional empirical likelihood for quantile regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(1), pages 1-16, January.
    • Handle: RePEc:spr:metrik:v:80:y:2017:i:1:d:10.1007_s00184-016-0588-6
    DOI: 10.1007/s00184-016-0588-6
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    References listed on IDEAS

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