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Determining The Degree Of Differencing For Time Series Via The Log Spectrum

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  • G. J. Janacek

Abstract

. While many time series require differencing before a model may be fitted it has been shown that ‘overdifferencing’ may result in a fitted model with poor long term forecasting properties. This may present real problems when the degree of differencing which is appropriate is fractional. We show that the log spectrum is a natural quantity to consider when attempting to determine the degree of differencing required and outline the distribution theory required. The ideas are shown to extend to the seasonal case and can be used to assess whether seasonal differencing is appropriate.

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  • G. J. Janacek, 1982. "Determining The Degree Of Differencing For Time Series Via The Log Spectrum," Journal of Time Series Analysis, Wiley Blackwell, vol. 3(3), pages 177-183, May.
    • Handle: RePEc:bla:jtsera:v:3:y:1982:i:3:p:177-183
    DOI: 10.1111/j.1467-9892.1982.tb00340.x
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    Cited by:

    1. Gourieroux Christian & Akonom, J., 1988. "Functional limit theorem for fractional processes (a)," CEPREMAP Working Papers (Couverture Orange) 8801, CEPREMAP.
    2. J. H. Wright, 1995. "Stochastic Orders Of Magnitude Associated With Two‐Stage Estimators Of Fractional Arima Systems," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(1), pages 119-125, January.
    3. B. Verspagen & G. Silverberg, 2000. "A note on Michelacci and Zaffaroni, long memory, and time series of economic growth," Working Papers 00.17, Eindhoven Center for Innovation Studies.
    4. Koop, Gary & Ley, Eduardo & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian analysis of long memory and persistence using ARFIMA models," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 149-169.
    5. Marc Hallin & Abdeslam Serroukh, 1998. "Adaptive Estimation of the Lag of a Long–memory Process," Statistical Inference for Stochastic Processes, Springer, vol. 1(2), pages 111-129, May.
    6. O. Mikhail & C. J. Eberwein & J. Handa, 2006. "Estimating persistence in Canadian unemployment: evidence from a Bayesian ARFIMA," Applied Economics, Taylor & Francis Journals, vol. 38(15), pages 1809-1819.
    7. Hurvich, Clifford M. & Moulines, Eric & Soulier, Philippe, 2002. "The FEXP estimator for potentially non-stationary linear time series," Stochastic Processes and their Applications, Elsevier, vol. 97(2), pages 307-340, February.
    8. Hurvich, Clifford M., 2002. "Multistep forecasting of long memory series using fractional exponential models," International Journal of Forecasting, Elsevier, vol. 18(2), pages 167-179.
    9. Lobato, I. & Robinson, P. M., 1996. "Averaged periodogram estimation of long memory," Journal of Econometrics, Elsevier, vol. 73(1), pages 303-324, July.
    10. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    11. Niels Haldrup & Oskar Knapik & Tommaso Proietti, 2016. "A generalized exponential time series regression model for electricity prices," CREATES Research Papers 2016-08, Department of Economics and Business Economics, Aarhus University.
    12. Ossama Mikhail & Curtis J. Eberwein & Jagdish Handa, 2003. "Testing and Estimating Persistence in Canadian Unemployment," Econometrics 0311004, University Library of Munich, Germany.
    13. Bollerslev, Tim & Wright, Jonathan H., 2000. "Semiparametric estimation of long-memory volatility dependencies: The role of high-frequency data," Journal of Econometrics, Elsevier, vol. 98(1), pages 81-106, September.
    14. Angela Ferretti & L. Ippoliti & P. Valentini & R. J. Bhansali, 2023. "Long memory conditional random fields on regular lattices," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.

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