文章基本信息

标题：Ratio Tests for Persistence Change with Heavy Tailed Observations
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作者：Yang, Yunfeng ; Jin, Hao
期刊名称：Journal of Networks
印刷版ISSN：1796-2056
出版年度：2014
卷号：9
期号：6
页码：1409-1415
DOI：10.4304/jnw.9.6.1409-1415
语种：English
出版社：Academy Publisher
摘要：This paper considers how to detect structural change in persistence between and behaviour with innovations in the domain of attraction of a -stable law. Conventional ratio-based tests developed in Kim [J. Econ. 95(2000)] are unreliable in the presence of such behavior, having non-pivotal asymptotic null distributions. In this paper we propose a subsampling approach to ratio-based tests that is valid against a range of heavy-tailed processes. Our proposed method does not require the practitioners to specify knowledge for stable index. Consistency and the rate of convergence for the estimated change point are also obtained. We show via simulations that our asymptotic results provide good approximations in finite samples.
关键词：Term Persistence Changes;Subsampling;Ratio-Based Tests;Heavy-Tailed