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Quantile regression for dynamic partially linear varying coefficient time series models

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  • Lian, Heng

Abstract

In this article, we consider quantile regression method for partially linear varying coefficient models for semiparametric time series modeling. We propose estimation methods based on general series estimation. We establish convergence rates of the estimator and the root-n asymptotic normality of the finite-dimensional parameter in the linear part. We further propose penalization-based method for automatically specifying the linear part of the model as well as performing variable selection, and show the model selection consistency of this approach. We illustrate the performance of estimates using a simulation study.

Suggested Citation

  • Lian, Heng, 2015. "Quantile regression for dynamic partially linear varying coefficient time series models," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 49-66.
    • Handle: RePEc:eee:jmvana:v:141:y:2015:i:c:p:49-66
    DOI: 10.1016/j.jmva.2015.06.013
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    References listed on IDEAS

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    Cited by:

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    2. Jun Zhang & Bingqing Lin & Yan Zhou, 2024. "Linear regression models with multiplicative distortions under new identifiability conditions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 78(1), pages 25-67, February.
    3. Chang-Sheng Liu & Han-Ying Liang, 2023. "Bayesian empirical likelihood of quantile regression with missing observations," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(3), pages 285-313, April.

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