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Adaptive estimation for varying coefficient models

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  • Chen, Yixin
  • Wang, Qin
  • Yao, Weixin

Abstract

In this article, a novel adaptive estimation is proposed for varying coefficient models. Unlike the traditional least squares based methods, the proposed approach can adapt to different error distributions. An efficient EM algorithm is provided to implement the proposed estimation. The asymptotic properties of the resulting estimator are established. Both simulation studies and real data examples are used to illustrate the finite sample performance of the new estimation procedure. The numerical results show that the gain of the new procedure over the least squares estimation can be quite substantial for non-Gaussian errors.

Suggested Citation

  • Chen, Yixin & Wang, Qin & Yao, Weixin, 2015. "Adaptive estimation for varying coefficient models," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 17-31.
    • Handle: RePEc:eee:jmvana:v:137:y:2015:i:c:p:17-31
    DOI: 10.1016/j.jmva.2015.01.017
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    3. Linton, Oliver & Xiao, Zhijie, 2019. "Efficient estimation of nonparametric regression in the presence of dynamic heteroskedasticity," Journal of Econometrics, Elsevier, vol. 213(2), pages 608-631.
    4. Fang Lu & Jing Yang & Xuewen Lu, 2022. "One-step oracle procedure for semi-parametric spatial autoregressive model and its empirical application to Boston housing price data," Empirical Economics, Springer, vol. 62(6), pages 2645-2671, June.

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