IDEAS home Printed from https://ideas.repec.org/a/spr/sistpr/v13y2010i3p163-174.html
   My bibliography  Save this article

Local Whittle likelihood estimators and tests for non-Gaussian stationary processes

Author

Listed:
  • Tomohito Naito
  • Kohei Asai
  • Tomoyuki Amano
  • Masanobu Taniguchi

Abstract

No abstract is available for this item.

Suggested Citation

  • Tomohito Naito & Kohei Asai & Tomoyuki Amano & Masanobu Taniguchi, 2010. "Local Whittle likelihood estimators and tests for non-Gaussian stationary processes," Statistical Inference for Stochastic Processes, Springer, vol. 13(3), pages 163-174, October.
    • Handle: RePEc:spr:sistpr:v:13:y:2010:i:3:p:163-174
    DOI: 10.1007/s11203-010-9044-9
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11203-010-9044-9
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11203-010-9044-9?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. Giraitis, Liudas & Robinson, Peter M., 2001. "Whittle Estimation Of Arch Models," Econometric Theory, Cambridge University Press, vol. 17(3), pages 608-631, June.
    2. Giraitis, Liudas & Robinson, Peter M., 2001. "Whittle estimation of ARCH models," LSE Research Online Documents on Economics 316, London School of Economics and Political Science, LSE Library.
    Full references (including those not matched with items on IDEAS)

    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. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
    2. Peter M Robinson & Paolo Zaffaroni, 2005. "Pseudo-Maximum Likelihood Estimation of ARCH(8) Models," STICERD - Econometrics Paper Series 495, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    3. Abdou Kâ Diongue & Dominique Guegan, 2008. "Estimation of k-factor GIGARCH process : a Monte Carlo study," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00235179, HAL.
    4. Zhang, Xinyu & Tong, Howell, 2022. "Asymptotic theory of principal component analysis for time series data with cautionary comments," LSE Research Online Documents on Economics 113566, London School of Economics and Political Science, LSE Library.
    5. Xinyu Zhang & Howell Tong, 2022. "Asymptotic theory of principal component analysis for time series data with cautionary comments," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 543-565, April.
    6. Wei, Honglei & Zhang, Hongfan & Jiang, Hui & Huang, Lei, 2022. "On the semi-varying coefficient dynamic panel data model with autocorrelated errors," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    7. Robinson, Peter M. & Zaffaroni, Paolo, 2005. "Pseudo-maximum likelihood estimation of ARCH(∞) models," LSE Research Online Documents on Economics 58182, London School of Economics and Political Science, LSE Library.
    8. Abdou Kâ Diongue & Dominique Guegan, 2004. "Estimating parameters for a k-GIGARCH process," Post-Print halshs-00188531, HAL.
    9. Tata Subba Rao & Granville Tunnicliffe Wilson & Joao Jesus & Richard E. Chandler, 2017. "Inference with the Whittle Likelihood: A Tractable Approach Using Estimating Functions," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 204-224, March.
    10. Xuejie Feng & Chiping Zhang, 2020. "A Perturbation Method to Optimize the Parameters of Autoregressive Conditional Heteroscedasticity Model," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 1021-1044, March.
    11. Giraitis, Liudas & Leipus, Remigijus & Robinson, Peter M. & Surgailis, Donatas, 2004. "LARCH, leverage, and long memory," LSE Research Online Documents on Economics 294, London School of Economics and Political Science, LSE Library.
    12. Royer, Julien, 2021. "Conditional asymmetry in Power ARCH($\infty$) models," MPRA Paper 109118, University Library of Munich, Germany.
    13. Jean-Marc Bardet & Paul Doukhan & José Rafael Leon_, 2005. "Uniform Limit Theorems for the Integrated Periodogram of Weakly Dependent Time Series and their Applications to Whittle's Estimate," Working Papers 2005-46, Center for Research in Economics and Statistics.
    14. Liudas Giraitis, 2004. "LARCH, Leverage, and Long Memory," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 177-210.
    15. Wang, Yudong & Wei, Yu & Wu, Chongfeng, 2010. "Auto-correlated behavior of WTI crude oil volatilities: A multiscale perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5759-5768.
    16. Christian Francq & Jean-Michel Zakoïan, 2008. "A Tour in the Asymptotic Theory of GARCH Estimation," Working Papers 2008-03, Center for Research in Economics and Statistics.
    17. Jean‐Marc Bardet & Paul Doukhan & José Rafael León, 2008. "Uniform limit theorems for the integrated periodogram of weakly dependent time series and their applications to Whittle's estimate," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(5), pages 906-945, September.
    18. Diongue Abdou Ka & Dominique Guegan, 2008. "Estimation of k-Factor Gigarch Process: A Monte Carlo Study," Post-Print halshs-00375758, HAL.
    19. Giraitis, Liudas & Leipus, Remigijus & Robinson, Peter M. & Surgailis, Donatas, 2003. "LARCH, leverage and long memory," LSE Research Online Documents on Economics 2020, London School of Economics and Political Science, LSE Library.
    20. repec:hum:wpaper:sfb649dp2006-033 is not listed on IDEAS
    21. Zaffaroni, Paolo, 2009. "Whittle estimation of EGARCH and other exponential volatility models," Journal of Econometrics, Elsevier, vol. 151(2), pages 190-200, August.

    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:spr:sistpr:v:13:y:2010:i:3:p:163-174. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.