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CQR-based inference for the infinite-variance nearly nonstationary autoregressive models

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Abstract

We consider the nearly nonstationary autoregressive model yt = qnyt−1+ut, where qn = 1−c/n, c is a fixed constant, and {ut, t ≽ 1} is a sequence of innovations belonging to the domain of attraction of a stable distribution with index 0 < α < 2. We construct a composite quantile regression estimator for the autoregressive coefficient and establish the asymptotic distribution of this estimator under some mild conditions.

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Authors and Affiliations

  1. Departmeng of Statistics, Zhejiang University City College, Hangzhou, 310015, China

    Ke-Ang Fu

  2. School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, 310018, China

    Jialin Ni & Yajuan Dong

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  1. Ke-Ang Fu
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  2. Jialin Ni
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  3. Yajuan Dong
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Correspondence to Yajuan Dong.

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This project was supported by the characteristic & preponderant discipline of key construction universities in Zhejiang province (Zhejiang Gongshang University - Statistics) and Collaborative Innovation Center of Statistical Data Engineering Technology & Application.

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Fu, KA., Ni, J. & Dong, Y. CQR-based inference for the infinite-variance nearly nonstationary autoregressive models. Lith Math J 62, 1–9 (2022). https://doi.org/10.1007/s10986-021-09539-4

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  • DOI: https://doi.org/10.1007/s10986-021-09539-4

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