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Robust discrimination between long‐range dependence and a change in mean

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  • Carina Gerstenberger

Abstract

In this article we introduce a robust to outliers Wilcoxon change‐point testing procedure, for distinguishing between short‐range dependent time series with a change in mean at unknown time and stationary long‐range dependent time series. We establish the asymptotic distribution of the test statistic under the null hypothesis for L1 near epoch dependent processes and show its consistency under the alternative. The Wilcoxon‐type testing procedure similarly as the CUSUM‐type testing procedure (of Berkes I., Horváth L., Kokoszka P. and Shao Q. 2006. Ann.Statist. 34:1140–1165), requires estimation of the location of a possible change‐point, and then using pre‐ and post‐break subsamples to discriminate between short and long‐range dependence. A simulation study examines the empirical size and power of the Wilcoxon‐type testing procedure in standard cases and with disturbances by outliers. It shows that in standard cases the Wilcoxon‐type testing procedure behaves equally well as the CUSUM‐type testing procedure but outperforms it in presence of outliers. We also apply both testing procedure to hydrologic data.

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  • Carina Gerstenberger, 2021. "Robust discrimination between long‐range dependence and a change in mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(1), pages 34-62, January.
    • Handle: RePEc:bla:jtsera:v:42:y:2021:i:1:p:34-62
    DOI: 10.1111/jtsa.12554
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    1. Dehling, Herold & Fried, Roland & Sharipov, Olimjon Sh. & Vogel, Daniel & Wornowizki, Max, 2013. "Estimation of the variance of partial sums of dependent processes," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 141-147.
    2. Granger, Clive W. J. & Hyung, Namwon, 2004. "Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 399-421, June.
    3. Carina Gerstenberger, 2018. "Robust Wilcoxon†Type Estimation of Change†Point Location Under Short†Range Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(1), pages 90-104, January.
    4. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    5. Seong Yeon Chang & Pierre Perron, 2016. "Inference on a Structural Break in Trend with Fractionally Integrated Errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(4), pages 555-574, July.
    6. Changryong Baek & Vladas Pipiras, 2012. "Statistical tests for a single change in mean against long‐range dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(1), pages 131-151, January.
    7. Zhongjun Qu, 2011. "A Test Against Spurious Long Memory," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 423-438, July.
    8. Mccloskey, Adam & Perron, Pierre, 2013. "Memory Parameter Estimation In The Presence Of Level Shifts And Deterministic Trends," Econometric Theory, Cambridge University Press, vol. 29(6), pages 1196-1237, December.
    9. Iacone, Fabrizio & Leybourne, Stephen J. & Taylor, A.M. Robert, 2019. "Testing The Order Of Fractional Integration Of A Time Series In The Possible Presence Of A Trend Break At An Unknown Point," Econometric Theory, Cambridge University Press, vol. 35(6), pages 1201-1233, December.
    10. Iacone, Fabrizio & Leybourne, Stephen J. & Robert Taylor, A.M., 2013. "Testing for a break in trend when the order of integration is unknown," Journal of Econometrics, Elsevier, vol. 176(1), pages 30-45.
    11. Hansen, Bruce E., 1991. "GARCH(1, 1) processes are near epoch dependent," Economics Letters, Elsevier, vol. 36(2), pages 181-186, June.
    12. Yanyuan Ma & Marc G. Genton, 2000. "Highly Robust Estimation of the Autocovariance Function," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(6), pages 663-684, November.
    13. Iacone, Fabrizio & Leybourne, Stephen J. & Taylor, A.M. Robert, 2013. "ON THE BEHAVIOR OF FIXED-b TREND BREAK TESTS UNDER FRACTIONAL INTEGRATION," Econometric Theory, Cambridge University Press, vol. 29(2), pages 393-418, April.
    14. Marc Lavielle & Eric Moulines, 2000. "Least‐squares Estimation of an Unknown Number of Shifts in a Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(1), pages 33-59, January.
    15. Wooldridge, Jeffrey M. & White, Halbert, 1988. "Some Invariance Principles and Central Limit Theorems for Dependent Heterogeneous Processes," Econometric Theory, Cambridge University Press, vol. 4(2), pages 210-230, August.
    16. Iacone Fabrizio & Leybourne Stephen J. & Robert Taylor A.M., 2017. "Testing for a Change in Mean under Fractional Integration," Journal of Time Series Econometrics, De Gruyter, vol. 9(1), pages 1-8, January.
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