Details
| Original language | English |
|---|---|
| Pages (from-to) | 237-256 |
| Number of pages | 20 |
| Journal | AStA Advances in Statistical Analysis |
| Volume | 103 |
| Issue number | 2 |
| Early online date | 26 May 2018 |
| Publication status | Published - 1 Jun 2019 |
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.
Keywords
- Fractional integration, Long memory, Structural breaks
ASJC Scopus subject areas
- Mathematics(all)
- Analysis
- Mathematics(all)
- Statistics and Probability
- Mathematics(all)
- Modelling and Simulation
- Social Sciences(all)
- Social Sciences (miscellaneous)
- Economics, Econometrics and Finance(all)
- Economics and Econometrics
- Mathematics(all)
- Applied Mathematics
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In: AStA Advances in Statistical Analysis, Vol. 103, No. 2, 01.06.2019, p. 237-256.
Research output: Contribution to journal › Review article › Research › peer review
}
TY - JOUR
T1 - Change-in-mean tests in long-memory time series
T2 - A review of recent developments
AU - Wenger, Kai
AU - Leschinski, Christian
AU - Sibbertsen, Philipp
N1 - Funding information: Financial support of the Deutsche Forschungsgesellschaft (DFG) is gratefully acknowledged. We would like to thank the anonymous referees for their reviews. We highly appreciate their comments and suggestions.
PY - 2019/6/1
Y1 - 2019/6/1
N2 - 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.
AB - 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.
KW - Fractional integration
KW - Long memory
KW - Structural breaks
UR - http://www.scopus.com/inward/record.url?scp=85047440039&partnerID=8YFLogxK
U2 - 10.1007/s10182-018-0328-5
DO - 10.1007/s10182-018-0328-5
M3 - Review article
AN - SCOPUS:85047440039
VL - 103
SP - 237
EP - 256
JO - AStA Advances in Statistical Analysis
JF - AStA Advances in Statistical Analysis
SN - 1863-8171
IS - 2
ER -