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Lag Window Estimation Of The Degree Of Differencing In Fractionally Integrated Time Series Models

Author

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  • Gemai Chen
  • Bovas Abraham
  • Shelton Peiris

Abstract

. In this paper we consider the estimation of the degree of differencing d in the fractionally integrated autoregressive moving‐average time series model ARFIMA (p, d, q). Using lag window spectral density estimators we develop a regression type estimator of d which is easy to calculate and does not require prior knowledge of p and q. Some large sample properties of the estimator are studied and the performance of the estimator for small samples is investigated using the simulation method for a range of commonly used lag windows. Some practical recommendations on the choice of lag windows and the choice of the window parameters are provided.

Suggested Citation

  • Gemai Chen & Bovas Abraham & Shelton Peiris, 1994. "Lag Window Estimation Of The Degree Of Differencing In Fractionally Integrated Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(5), pages 473-487, September.
    • Handle: RePEc:bla:jtsera:v:15:y:1994:i:5:p:473-487
    DOI: 10.1111/j.1467-9892.1994.tb00205.x
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    Cited by:

    1. Christian Bordes & Éric Girardin & Velayoudom Marimoutou, 1996. "Le nouveau SME est-il plus asymétrique que l'ancien ?," Économie et Prévision, Programme National Persée, vol. 123(2), pages 175-188.
    2. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    3. Chareka, Patrick & Matarise, Florance & Turner, Rolf, 2006. "A test for additive outliers applicable to long-memory time series," Journal of Economic Dynamics and Control, Elsevier, vol. 30(4), pages 595-621, April.
    4. Ana Pérez & Esther Ruiz, 2002. "Modelos de memoria larga para series económicas y financieras," Investigaciones Economicas, Fundación SEPI, vol. 26(3), pages 395-445, September.
    5. Claude Diebolt & Vivien Guiraud, 2005. "A Note On Long Memory Time Series," Quality & Quantity: International Journal of Methodology, Springer, vol. 39(6), pages 827-836, December.
    6. Shitan, Mahendran & Peiris, Shelton, 2009. "On properties of the second order generalized autoregressive GAR(2) model with index," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(2), pages 367-377.
    7. Erhard Reschenhofer & Manveer K. Mangat, 2021. "Fast computation and practical use of amplitudes at non-Fourier frequencies," Computational Statistics, Springer, vol. 36(3), pages 1755-1773, September.
    8. Erhard Reschenhofer & Manveer K. Mangat, 2020. "Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data," Econometrics, MDPI, vol. 8(4), pages 1-15, October.
    9. Dissanayake, G.S. & Peiris, M.S. & Proietti, T., 2016. "State space modeling of Gegenbauer processes with long memory," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 115-130.
    10. Fajardo, Fabio Alexander, 2011. "Some Alternatives for Robust Estimation of the Spectrum in Stationary Processes," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(1), March.

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