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Adaptive estimation for varying coefficient models with nonstationary covariates

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  • Zhiyong Zhou
  • Jun Yu

Abstract

In this paper, the adaptive estimation for varying coefficient models proposed by Chen, Wang, and Yao (2015) is extended to allowing for nonstationary covariates. The asymptotic properties of the estimator are obtained, showing different convergence rates for the integrated covariates and stationary covariates. The nonparametric estimator of the functional coefficient with integrated covariates has a faster convergence rate than the estimator with stationary covariates, and its asymptotic distribution is mixed normal. Moreover, the adaptive estimation is more efficient than the least square estimation for non normal errors. A simulation study is conducted to illustrate our theoretical results.

Suggested Citation

  • Zhiyong Zhou & Jun Yu, 2019. "Adaptive estimation for varying coefficient models with nonstationary covariates," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(16), pages 4034-4050, August.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:16:p:4034-4050
    DOI: 10.1080/03610926.2018.1484483
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