Geometric ergodicity and conditional self-weighted M-estimator of a GRCAR(p) model with heavy-tailed errors

作者全名："Li, Xiaoyan; Pan, Jiazhu; Song, Anchao"

作者地址："[Li, Xiaoyan] Chongqing Univ, Coll Math & Stat, Chongqing 401331, Peoples R China; [Pan, Jiazhu] Univ Strathclyde, Dept Math & Stat, Glasgow G1 1XH, Scotland; [Song, Anchao] Chongqing Med Univ, Sch Publ Hlth & Management, Chongqing 400016, Peoples R China; [Pan, Jiazhu] Univ Strathclyde, Dept Math & Stat, 26 Richmond St, Glasgow G1 1XH, Scotland"

通信作者："Pan, JZ (通讯作者)，Univ Strathclyde, Dept Math & Stat, 26 Richmond St, Glasgow G1 1XH, Scotland."

来源：JOURNAL OF TIME SERIES ANALYSIS

ESI学科分类：MATHEMATICS

WOS号：WOS:000928655700001

JCR分区：Q2

影响因子：1.2

年份：2023

卷号：　

期号：　

开始页：　

结束页：　

文献类型：Article; Early Access

关键词：asymptotic normality; generalized random coefficient autoregressive model; geometric ergodicity; self-weighted M-estimator; stochastic functional autoregression

摘要："We establish the geometric ergodicity for general stochastic functional autoregressive (linear and nonlinear) models with heavy-tailed errors. The stationarity conditions for a generalized random coefficient autoregressive model (GRCAR(p)) are presented as a corollary. And then, a conditional self-weighted M-estimator for parameters in the GRCAR(p) is proposed. The asymptotic normality of this estimator is discussed by allowing infinite variance innovations. Simulation experiments are carried out to assess the finite-sample performance of the proposed methodology and theory, and a real heavy-tailed data example is given as illustration."

基金机构：National Natural Science Foundation of China [12171161]

基金资助正文："ACKNOWLEDGEMENTS The authors thank the Editor, the Co-Editor and the Referee(s) for their insightful comments and suggestions that make us improve our article significantly. The second author's work was partially supported by the National Natural Science Foundation of China (Grant No. 12171161)."