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A comparison of techniques of estimation in long-memory processes

Author

Listed:
  • Luisa Bisaglia

    (Departement of Statistics - Unipd - Università degli Studi di Padova = University of Padua)

  • Dominique Guegan

    (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique)

Abstract

In this paper we discuss the properties of most important estimators of long-range dependence parameters. We compare the properties of these estimators via Monte Carlo experiments. We give an empirical approach for confidence intervals for the different parameter estimates. We then apply these procedures to a real time series to investigate its long-memory properties.

Suggested Citation

  • Luisa Bisaglia & Dominique Guegan, 1998. "A comparison of techniques of estimation in long-memory processes," Post-Print halshs-00194462, HAL.
    • Handle: RePEc:hal:journl:halshs-00194462
    DOI: 10.1016/S0167-9473(97)00045-5
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    Citations

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    Cited by:

    1. McCoy, E. J. & Stephens, D. A., 2004. "Bayesian time series analysis of periodic behaviour and spectral structure," International Journal of Forecasting, Elsevier, vol. 20(4), pages 713-730.
    2. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Bandwidth selection by cross-validation for forecasting long memory financial time series," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 129-143.
    3. Fantazzini, Dean, 2023. "Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models," MPRA Paper 117141, University Library of Munich, Germany.
    4. Grassi, Stefano & Santucci de Magistris, Paolo, 2014. "When long memory meets the Kalman filter: A comparative study," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 301-319.
    5. Luisa Bisaglia & Silvano Bordignon, 2002. "Mean square prediction error for long-memory processes," Statistical Papers, Springer, vol. 43(2), pages 161-175, April.
    6. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Modified information criteria and selection of long memory time series models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 116-131.
    7. Pai, Jeffrey & Ravishanker, Nalini, 2015. "Fast approximate likelihood evaluation for stable VARFIMA processes," Statistics & Probability Letters, Elsevier, vol. 103(C), pages 160-168.
    8. Reisen Valderio A & Cribari-Neto Francisco & Jensen Mark J, 2003. "Long Memory Inflationary Dynamics: The Case of Brazil," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(3), pages 1-18, October.

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