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Monitoring memory parameter change-points in long-memory time series

Zhanshou Chen (), Yanting Xiao and Fuxiao Li
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Zhanshou Chen: Qinghai Normal University
Yanting Xiao: Xi’an University of Technology
Fuxiao Li: Xi’an University of Technology

Empirical Economics, 2021, vol. 60, issue 5, No 9, 2365-2389

Abstract: Abstract In this paper, we propose two ratio-type statistics to sequentially detect the memory parameter change-points in the long-memory time series. The limiting distributions of monitoring statistics under the no-change-point null hypothesis as well as their consistency under the alternative hypothesis are proved. In particular, a sieve bootstrap approximation method is proposed to determine the critical values. Extensive simulations indicate that the new monitoring procedures perform well in finite samples. Finally, we illustrate our monitoring procedures by two sets of real data.

Keywords: Long-memory process; Change-point monitoring; Sieve bootstrap; Fractional Brownian motion (search for similar items in EconPapers)
JEL-codes: C12 C15 C22 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00181-020-01840-4

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