IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v80y2010i10p2026-2032.html
   My bibliography  Save this article

Strong convergence rate of robust estimator of change point

Author

Listed:
  • Qin, Ruibing
  • Tian, Zheng
  • Jin, Hao
  • Zhang, Xiaowei

Abstract

This paper considers a mean shift with a unknown change point in α-mixing processes with κ stable innovations and estimates the unknown change point by the robust nonparametric CUSUM estimator based on the indicators of the data minus the sample median. The strong convergence rate of the estimator is obtained, which is not affected by the characteristic index κ. We also develop two algorithms for the estimate of change point based on the proposed CUSUM estimator. Simulations demonstrate that the estimator behaves well for heavy-tailed sequences.

Suggested Citation

  • Qin, Ruibing & Tian, Zheng & Jin, Hao & Zhang, Xiaowei, 2010. "Strong convergence rate of robust estimator of change point," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(10), pages 2026-2032.
    • Handle: RePEc:eee:matcom:v:80:y:2010:i:10:p:2026-2032
    DOI: 10.1016/j.matcom.2010.02.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475410000820
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2010.02.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cheng, Tsung-Lin, 2009. "An efficient algorithm for estimating a change-point," Statistics & Probability Letters, Elsevier, vol. 79(5), pages 559-565, March.
    2. Horváth, Lajos & Kokoszka, Piotr, 2003. "A bootstrap approximation to a unit root test statistic for heavy-tailed observations," Statistics & Probability Letters, Elsevier, vol. 62(2), pages 163-173, April.
    3. de Jong, Robert M. & Amsler, Christine & Schmidt, Peter, 2007. "A robust version of the KPSS test based on indicators," Journal of Econometrics, Elsevier, vol. 137(2), pages 311-333, April.
    4. Shi, Xiaoping & Wu, Yuehua & Miao, Baiqi, 2009. "Strong convergence rate of estimators of change point and its application," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 990-998, February.
    5. Cappelli, Carmela & Penny, Richard N. & Rea, William S. & Reale, Marco, 2008. "Detecting multiple mean breaks at unknown points in official time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 351-356.
    6. Chen, Cathy W.S. & Gerlach, Richard & Cheng, Nick Y.P. & Yang, Y.L., 2009. "The impact of structural breaks on the integration of the ASEAN-5 stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2654-2664.
    7. Zhou, Jie & Liu, San Y., 2009. "Inference for mean change-point in infinite variance AR(p) process," Statistics & Probability Letters, Elsevier, vol. 79(1), pages 6-15, January.
    8. Phillips, P.C.B., 1990. "Time Series Regression With a Unit Root and Infinite-Variance Errors," Econometric Theory, Cambridge University Press, vol. 6(1), pages 44-62, March.
    9. Maekawa, Koichi & He, Zonglu & Tee, Kianheng, 2004. "Estimating break points in a time series regression with structural changes," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(1), pages 95-101.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cheng, Tsung-Lin & Wang, Jheng-Ting, 2020. "A computationally efficient approach on detecting star-shaped change boundaries in random fields with heavy-tailed distributions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 169(C), pages 16-25.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Serttas, Fatma Ozgu, 2010. "Essays on infinite-variance stable errors and robust estimation procedures," ISU General Staff Papers 201001010800002742, Iowa State University, Department of Economics.
    2. Guili Liao & Qimeng Liu & Rongmao Zhang & Shifang Zhang, 2022. "Rank test of unit‐root hypothesis with AR‐GARCH errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(5), pages 695-719, September.
    3. Simões, Oscar R. & Marçal, Emerson Fernandes, 2012. "Agregação temporal e não-linearidade afetam os testes da paridade do poder de compra: Evidência a partir de dados brasileiros," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 66(3), October.
    4. Nikolaos Kourogenis & Nikitas Pittis, 2008. "Testing for a unit root under errors with just barely infinite variance," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(6), pages 1066-1087, November.
    5. Claudiu Tiberiu Albulescu, 2022. "Health Care Expenditure in the European Union Countries: New Insights about the Convergence Process," IJERPH, MDPI, vol. 19(4), pages 1-16, February.
    6. Lorenzo Trapani, 2021. "Testing for strict stationarity in a random coefficient autoregressive model," Econometric Reviews, Taylor & Francis Journals, vol. 40(3), pages 220-256, April.
    7. Miller, J. Isaac & Park, Joon Y., 2005. "How They Interact to Generate Persistency in Memory," Working Papers 2005-01, Rice University, Department of Economics.
    8. Xiao, Zhijie, 2012. "Robust inference in nonstationary time series models," Journal of Econometrics, Elsevier, vol. 169(2), pages 211-223.
    9. Caner, Mehmet, 1998. "Tests for cointegration with infinite variance errors," Journal of Econometrics, Elsevier, vol. 86(1), pages 155-175, June.
    10. Kim, Jihyun & Meddahi, Nour, 2020. "Volatility regressions with fat tails," Journal of Econometrics, Elsevier, vol. 218(2), pages 690-713.
    11. Fatma Ozgu Serttas, 2018. "Infinite-Variance Error Structure in Finance and Economics," International Econometric Review (IER), Econometric Research Association, vol. 10(1), pages 14-23, April.
    12. Fabio Busetti & Andrew Harvey, 2011. "When is a Copula Constant? A Test for Changing Relationships," Journal of Financial Econometrics, Oxford University Press, vol. 9(1), pages 106-131, Winter.
    13. Kurz-Kim, Jeong-Ryeol & Loretan, Mico, 2014. "On the properties of the coefficient of determination in regression models with infinite variance variables," Journal of Econometrics, Elsevier, vol. 181(1), pages 15-24.
    14. Hao Jin & Si Zhang & Jinsuo Zhang, 2017. "Spurious regression due to neglected of non-stationary volatility," Computational Statistics, Springer, vol. 32(3), pages 1065-1081, September.
    15. repec:fgv:epgrbe:v:66:n:3:a:6 is not listed on IDEAS
    16. Allen, David E. & Gao, Jiti & McAleer, Michael, 2009. "Modelling and managing financial risk: An overview," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2521-2524.
    17. Cappuccio, Nunzio & Lubian, Diego & Mistrorigo, Mirko, 2015. "The power of unit root tests under local-to-finite variance errors," Chaos, Solitons & Fractals, Elsevier, vol. 76(C), pages 205-217.
    18. Datta, Somnath, 1995. "Limit theory and bootstrap for explosive and partially explosive autoregression," Stochastic Processes and their Applications, Elsevier, vol. 57(2), pages 285-304, June.
    19. Kang, Sang Hoon & Uddin, Gazi Salah & Troster, Victor & Yoon, Seong-Min, 2019. "Directional spillover effects between ASEAN and world stock markets," Journal of Multinational Financial Management, Elsevier, vol. 52.
    20. Pelagatti, Matteo M. & Sen, Pranab K., 2013. "Rank tests for short memory stationarity," Journal of Econometrics, Elsevier, vol. 172(1), pages 90-105.
    21. D. M. Mahinda Samarakoon & Keith Knight, 2009. "A Note on Unit Root Tests with Infinite Variance Noise," Econometric Reviews, Taylor & Francis Journals, vol. 28(4), pages 314-334.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:matcom:v:80:y:2010:i:10:p:2026-2032. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.