Acta mathematica scientia,Series A ›› 2020, Vol. 40 ›› Issue (2): 475-483.

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Asymptotics for the Self-Weighted M-Estimation of Nonlinear Autoregressive Models with Heavy-Tailed Errors

Keang Fu(),Li Ding,Junqiao Li   

  1. School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018
  • Received:2018-09-27 Online:2020-04-26 Published:2020-05-21
  • Supported by:
    the NSFC(11971432);the NSF of Zhejiang Province(LY17A010004);the First Class Discipline of Zhejiang-A(浙江工商大学统计学)

Abstract:

Consider the nonlinear autoregressive model xt=f(xt-1, …, xt-p, θ)+εt, where θ is the q-dimensional unknown parameter and εt's are random errors with possibly infinite variance. In this paper, the self-weighted M-estimator of θ is constructed, and the asymptotic normality of the proposed estimator is also established. Some simulation studies are also given to show that the self-weighted M-estimators have good performances with some heavy-tailed random errors.

Key words: Nonlinear autoregression, Self-weighted M-estimator, Heavy tail, Asymptotic normality

CLC Number: 

  • O212.1
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