Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/172852
Authors: 
Wenger, Kai
Leschinski, Christian
Sibbertsen, Philipp
Year of Publication: 
2017
Series/Report no.: 
Hannover Economic Papers (HEP) 598
Abstract: 
It is well known that standard tests for a mean shift are invalid in long-range dependent time series. Therefore, several long memory robust extensions of standard testing principles for a change-in-mean have been proposed in the literature. These can be divided into two groups: those that utilize consistent estimates of the long-run variance and self-normalized test statistics. Here, we review this literature and complement it by deriving a new long memory robust version of the sup-Wald test. Apart from giving a systematic review, we conduct an extensive Monte Carlo study to compare the relative performance of these methods. Special attention is paid to the interaction of the test results with the estimation of the long-memory parameter. Furthermore, we show that the power of self-normalized test statistics can be improved considerably by using an estimator that is robust to mean shifts.
Subjects: 
Fractional Integration
Structural Breaks
Long Memory
JEL: 
C12
C22
Document Type: 
Working Paper

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