|Title:||Estimation methods for the LRD parameter under a change in the mean|
|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|
|Appears in Collections:||Sonderforschungsbereich (SFB) 823|
Files in This Item:
|DP_3216_SFB823_Rooch_Zelo_Fried.pdf||DNB||455.32 kB||Adobe PDF||View/Open|
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