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Orthogonality-projection-based estimation for semi-varying coefficient models with heteroscedastic errors

Author

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  • Zhao, Yan-Yong
  • Lin, Jin-Guan
  • Xu, Pei-Rong
  • Ye, Xu-Guo

Abstract

This paper is concerned with the estimation in semi-varying coefficient models with heteroscedastic errors. An iterated two-stage orthogonality-projection-based estimation is proposed. This method can easily be used to estimate the model parametric and nonparametric parts, as well as the variance function, and in the estimators the parametric part and nonparametric part do not affect each other. Under some mild conditions, the consistency, conditional biases, conditional variances and asymptotic normality of the resulting estimators are studied explicitly. Moreover, some simulation studies are carried out to examine the finite sample performance of the proposed methods. Finally, the methodologies are illustrated by a real data set.

Suggested Citation

  • Zhao, Yan-Yong & Lin, Jin-Guan & Xu, Pei-Rong & Ye, Xu-Guo, 2015. "Orthogonality-projection-based estimation for semi-varying coefficient models with heteroscedastic errors," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 204-221.
    • Handle: RePEc:eee:csdana:v:89:y:2015:i:c:p:204-221
    DOI: 10.1016/j.csda.2015.03.018
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    References listed on IDEAS

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    Cited by:

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    2. Peng, Heng & Xie, Chuanlong & Zhao, Jingxin, 2021. "Fast inference for semi-varying coefficient models via local averaging," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    3. Zhao, Yan-Yong & Lin, Jin-Guan, 2019. "Estimation and test of jump discontinuities in varying coefficient models with empirical applications," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 145-163.

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