Spatial quantile estimation of multivariate threshold time series models

被引：2
|
作者：
Jiang, Jiancheng [1 ]
Jiang, Xuejun [2 ]
Li, Jingzhi [2 ]
Liu, Yi [1 ]
Yan, Wanfeng [2 ,3 ]
机构：
[1] Univ North Carolina Charlotte, Dept Math & Stat, Charlotte, NC USA
[2] Southern Univ Sci & Technol, Dept Math, Shenzhen 518055, Peoples R China
[3] Banque Pictet & Cie SA, Route Acacias 60, CH-1211 Geneva 73, Switzerland
来源：
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2017年 / 486卷
关键词：
Spatial quantile regression; Vector time series; Multivariate threshold time series models; ARCH; AUTOREGRESSION; ROBUST;
D O I：
10.1016/j.physa.2017.05.062
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
In this paper we study spatial quantile regression estimation of multivariate threshold time series models. Bahadur's representations for our estimators are established, which naturally lead to asymptotic normality of the estimators. Simulations and a real example are used to evaluate the performance of the proposed estimators. (C) 2017 Elsevier B.U. All rights reserved.
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页码：772 / 781
页数：10
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