Authors: Rooch, Aeneas
Zelo, Ieva
Fried, Roland
Title: Estimation methods for the LRD parameter under a change in the mean
Language (ISO): en
Abstract: When analyzing time series which are supposed to exhibit long-range dependence (LRD), a basic issue is the estimation of the LRD parameter, for example the Hurst parameter H 2 (1=2; 1). Conventional estimators of H easily lead to spurious detection of long memory if the time series includes a shift in the mean. This defect has fatal consequences in change-point problems: Tests for a level shift rely on H, which needs to be estimated before, but this estimation is distorted by the level shift. We investigate two blocks approaches to adapt estimators of H to the case that the time series includes a jump and compare them with other natural techniques as well as with estimators based on the trimming idea via simulations. These techniques improve the estimation of H if there is indeed a change in the mean. In the absence of such a change, the methods little affect the usual estimation. As adaption, we recommend an overlapping blocks approach: If one uses a consistent estimator, the adaption will preserve this property and it performs well in simulations.
Subject Headings: Hurst parameter
change-point problems
long memory
long-range dependence
Issue Date: 2016
Appears in Collections:Sonderforschungsbereich (SFB) 823

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