Robust bootstrap estimates in heteroscedastic semi-varying coefficient models and applications in analyzing Australia CPI data

被引:2
作者
Zhao, Yan-Yong [1 ]
Lin, Jin-Guan [1 ]
Wang, Hong-Xia [1 ]
机构
[1] Nanjing Audit Univ, Dept Stat, Nanjing 211815, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Heteroscedasticity; Profile least-squares; Robust bootstrap estimator; Semi-varying coefficient models; EFFICIENT ESTIMATION; VARIABLE SELECTION; LIKELIHOOD;
D O I
10.1080/03610918.2015.1054940
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
This article deals with the estimation of the parametric component, which is of primary interest, in the heteroscedastic semi-varying coefficient models. Based on the bootstrap technique, we present a procedure for estimating the parameters, which can provide a reliable approximation to the asymptotic distribution of the profile least-square (PLS) estimator. Furthermore, a bootstrap-type estimator of covariance matrix is developed, which is proved to be a consistent estimator of the covariance matrix. Moreover, some simulation experiments are conducted to evaluate the finite sample performance for the proposed methodology. Finally, the Australia CPI dataset is analyzed to demonstrate the application of the methods.
引用
收藏
页码:2638 / 2653
页数:16
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