Sieve bootstrap monitoring persistence change in long memory process

• DOI: 10.4310/sii.2016.v9.n1.a4
• Publication Date: 2015-10-22T20:24:52Z
• Authors: Zhanshou Chen; Zheng Tian; Yuhong Xing
• Keywords: Sieve (category theory); Persistence (discontinuity)

This paper adopts a moving ratio statistic to monitor persistence change in long memory process.The limiting distribution of monitoring under the stationary null hypothesis is derived.We show that proposed scheme consistent for nonstationary change.In particular, sieve bootstrap approximation method proposed.The used determine critical values which depends on unknown parameter.The empirical size, power and average run length procedure are evaluated simulation study.Simulations indicate new performs well finite samples.Finally, we illustrate our using set foreign exchange rate data.