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

Li, Xiaoyan and Pan, Jiazhu and Song, Anchao (2023) Geometric ergodicity and conditional self-weighted M-estimator of a GRCAR(p) model with heavy‐tailed errors. Journal of Time Series Analysis. pp. 1-19. ISSN 0143-9782 (https://doi.org/10.1111/jtsa.12680)

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Abstract

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.

ORCID iDs

Li, Xiaoyan, Pan, Jiazhu ORCID logoORCID: https://orcid.org/0000-0001-7346-2052 and Song, Anchao;
  • Item type:Article
    ID code:83843
    Dates:
    Date
    Event
    19 January 2023
    Published
    19 January 2023
    Published Online
    13 January 2023
    Accepted
    Keywords:stochastic functional autoregression, generalized random coefficient autoregressive model, geometric ergodicity, self-weighted M-estimator, asymptotic normality, Mathematics, Mathematics(all), Economics, Econometrics and Finance(all)
    Subjects:Science > Mathematics
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:26 Jan 2023 11:05
    Last modified:18 Mar 2023 04:07
    URI:https://strathprints.strath.ac.uk/id/eprint/83843
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