Estimation and inference of semi-varying coefficient models with heteroscedastic errors

JOURNAL OF MULTIVARIATE ANALYSIS

卷:124

Article

Shen, Si-Lian¹  Mei, Chang-Lin²  Wang, Chun-Wei¹ 

[1] Henan Univ Sci & Technol, Sch Math & Stat, Luoyang, Peoples R China

[2] Xi An Jiao Tong Univ, Sch Sci, Xian 710049, Peoples R China

关键词: Semi-varying coefficient model;    Heteroscedasticity;    Local linear smoothing;    Re-weighting estimation;   

DOI  :  10.1016/j.jmva.2013.10.010

来源: Elsevier

【 摘 要 】

This article focuses on the estimation of the parametric component, which is of primary interest, in semi-varying coefficient models with heteroscedastic errors. Specifically, we first present a procedure for estimating the variance function of the error term and the resulting estimator is proved to be consistent. Then, by applying the local linear smoothing technique, and taking the estimated error heteroscedasticity into account, we suggest a re-weighting estimation of the constant coefficients and the resulting estimators are shown to have smaller asymptotic variances than the profile least-squares estimators that neglect the error heteroscedasticity while remaining the same biases. Some simulation experiments are conducted to evaluate the finite sample performance of the proposed methodologies. Finally, a real-world data set is analyzed to demonstrate the application of the methods. (C) 2013 Elsevier Inc. All rights reserved.

【 授权许可】

Free