IDEAS home Printed from https://ideas.repec.org/p/eca/wpaper/2013-156105.html
   My bibliography  Save this paper

Quantile Spectral Processes: Asymptotic Analysis and Inference

Author

Listed:
  • Tobias Kley
  • Stanislav Volgushev
  • Holger Dette
  • Marc Hallin

Abstract

No abstract is available for this item.

Suggested Citation

  • Tobias Kley & Stanislav Volgushev & Holger Dette & Marc Hallin, 2014. "Quantile Spectral Processes: Asymptotic Analysis and Inference," Working Papers ECARES ECARES 2014-07, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:eca:wpaper:2013/156105
    as

    Download full text from publisher

    File URL: https://dipot.ulb.ac.be/dspace/bitstream/2013/156105/1/2014-07-KLEY_VOLGUSHEV_DETTE_HALLIN-quantile.pdf
    File Function: 2014-07-KLEY_VOLGUSHEV_DETTE_HALLIN-quantile
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers CWP36/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Stefan Birr & Stanislav Volgushev & Tobias Kley & Holger Dette & Marc Hallin, 2017. "Quantile spectral analysis for locally stationary time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1619-1643, November.
    3. Yuichi Goto & Tobias Kley & Ria Van Hecke & Stanislav Volgushev & Holger Dette & Marc Hallin, 2021. "The Integrated Copula Spectrum," Working Papers ECARES 2021-29, ULB -- Universite Libre de Bruxelles.
    4. Jozef Baruník & Tobias Kley, 2019. "Quantile coherency: A general measure for dependence between cyclical economic variables," The Econometrics Journal, Royal Economic Society, vol. 22(2), pages 131-152.
    5. B. Lafuente-Rego & P. D’Urso & J. A. Vilar, 2020. "Robust fuzzy clustering based on quantile autocovariances," Statistical Papers, Springer, vol. 61(6), pages 2393-2448, December.
    6. Galvao, Antonio F. & Gu, Jiaying & Volgushev, Stanislav, 2020. "On the unbiased asymptotic normality of quantile regression with fixed effects," Journal of Econometrics, Elsevier, vol. 218(1), pages 178-215.
    7. Yuichi Goto & Masanobu Taniguchi, 2020. "Discriminant analysis based on binary time series," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(5), pages 569-595, July.
    8. Stefan Birr & Holger Dette & Marc Hallin & Tobias Kley & Stanislav Volgushev, 2016. "On Wigner-Ville Spectra and the Unicity of Time-Varying Quantile-Based Spectral Densities," Working Papers ECARES ECARES 2016-38, ULB -- Universite Libre de Bruxelles.
    9. Kley, Tobias, 2016. "Quantile-Based Spectral Analysis in an Object-Oriented Framework and a Reference Implementation in R: The quantspec Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i03).
    10. Zhang, Shibin, 2019. "Bayesian copula spectral analysis for stationary time series," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 166-179.
    11. Baumöhl, Eduard & Vyrost, Tomas, 2020. "Stablecoins as a crypto safe haven? Not all of them!," EconStor Preprints 215484, ZBW - Leibniz Information Centre for Economics.
    12. Chen, Tianbo & Sun, Ying & Li, Ta-Hsin, 2021. "A semi-parametric estimation method for the quantile spectrum with an application to earthquake classification using convolutional neural network," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    13. Yan Liu, 2017. "Statistical inference for quantiles in the frequency domain," Statistical Inference for Stochastic Processes, Springer, vol. 20(3), pages 369-386, October.
    14. Yaeji Lim & Hee-Seok Oh, 2016. "Composite Quantile Periodogram for Spectral Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(2), pages 195-221, March.

    More about this item

    Keywords

    time series; spectral analysis; periodogram; quantiles; copulas; ranks; spearman; blomqvist; gini spectra;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:eca:wpaper:2013/156105. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Benoit Pauwels (email available below). General contact details of provider: https://edirc.repec.org/data/arulbbe.html .

    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.