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Two-step estimation of time-varying additive model for locally stationary time series

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  • Hu, Lixia
  • Huang, Tao
  • You, Jinhong

Abstract

In the analysis of locally stationary process, a time-varying additive model (tvAM) can effectively capture the dynamic feature of regression function. In combination with the strengths of tensor product of B-spline smoothing and local linear smoothing method, a two-step estimation method is proposed. It is shown that the proposed estimator is uniformly consistent and asymptotically oracle efficient as if the other component functions were known. Furthermore, a nonparametric bootstrap procedure is proposed to test the time-varying property of regression function. Simulation studies investigate the finite-sample performance of the proposed methods and validate the asymptotic theory. An environmental dataset illustrating the proposed method is also considered.

Suggested Citation

  • Hu, Lixia & Huang, Tao & You, Jinhong, 2019. "Two-step estimation of time-varying additive model for locally stationary time series," Computational Statistics & Data Analysis, Elsevier, vol. 130(C), pages 94-110.
    • Handle: RePEc:eee:csdana:v:130:y:2019:i:c:p:94-110
    DOI: 10.1016/j.csda.2018.08.023
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