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Variable selection in partially time-varying coefficient models

Author

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  • Degui Li
  • Jia Chen
  • Zhengyan Lin

Abstract

A partially time-varying coefficient model is introduced to characterise the nonlinearity and trending phenomenon. To enhance predictability and to select significant variables in the parametric component of the model, the penalised least squares method with the help of the profile local linear technique is developed in this article. The convergence rate and the oracle property of the resulting estimator are established under mild conditions. A remarkable achievement of our results is that it does not require undersmoothing of the nonparametric component. Meanwhile, some extensions of the proposed model and method are also discussed. Furthermore, some numerical examples are provided to show that our theory and method work well in practice.

Suggested Citation

  • Degui Li & Jia Chen & Zhengyan Lin, 2009. "Variable selection in partially time-varying coefficient models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(5), pages 553-566.
    • Handle: RePEc:taf:gnstxx:v:21:y:2009:i:5:p:553-566
    DOI: 10.1080/10485250902912694
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    References listed on IDEAS

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    Cited by:

    1. Ruiqi Liu & Ben Boukai & Zuofeng Shang, 2019. "Statistical Inference on Partially Linear Panel Model under Unobserved Linearity," Papers 1911.08830, arXiv.org.
    2. Zhang, Jun & Feng, Zhenghui & Peng, Heng, 2018. "Estimation and hypothesis test for partial linear multiplicative models," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 87-103.
    3. Zheng, Zemin & Li, Yang & Yu, Chongxiu & Li, Gaorong, 2018. "Balanced estimation for high-dimensional measurement error models," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 78-91.
    4. Zhang, Ting, 2015. "Semiparametric model building for regression models with time-varying parameters," Journal of Econometrics, Elsevier, vol. 187(1), pages 189-200.

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