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Comparaison of several estimation procedures for long term behavior

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In this paper, nine memory parameter estimation procedures for the fractionally integrated I(d) process, semi-parametric and parametric, which prevail in the existing literature are reviewed; through the simulation study under the ARFIMA (p,d,q) setting we cast a light on the finite sample performance of these estimation procedures for the non-stationary long memory time series. As a by-product of this study, we provide a bandwidth parameter selection strategy for the frequency domain estimation and an upper-and-lower scale trimming strategy for the wavelet domain estimation from a practical stand-point. The other objective of this paper is to give a useful reference to the applied reserachers and practitioners

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  • Dominique Guegan & Zhiping Lu & BeiJia Zhu, 2012. "Comparaison of several estimation procedures for long term behavior," Documents de travail du Centre d'Economie de la Sorbonne 12008, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    • Handle: RePEc:mse:cesdoc:12008
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    1. Katsumi Shimotsu & Peter C.B. Phillips, 2000. "Modified Local Whittle Estimation of the Memory Parameter in the Nonstationary Case," Cowles Foundation Discussion Papers 1265, Cowles Foundation for Research in Economics, Yale University.
    2. Shimotsu, Katsumi, 2002. "Exact Local Whittle Estimation of Fractional Integration with Unknown Mean and Time Trend," Economics Discussion Papers 8844, University of Essex, Department of Economics.
    3. Frederiksen, Per & Nielsen, Frank S. & Nielsen, Morten Ørregaard, 2012. "Local polynomial Whittle estimation of perturbed fractional processes," Journal of Econometrics, Elsevier, vol. 167(2), pages 426-447.
    4. Peter C.B. Phillips, 1999. "Discrete Fourier Transforms of Fractional Processes," Cowles Foundation Discussion Papers 1243, Cowles Foundation for Research in Economics, Yale University.
    5. Morten Ørregaard Nielsen & Per Houmann Frederiksen, 2005. "Finite Sample Comparison of Parametric, Semiparametric, and Wavelet Estimators of Fractional Integration," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 405-443.
    6. Abadir, Karim M. & Distaso, Walter & Giraitis, Liudas, 2007. "Nonstationarity-extended local Whittle estimation," Journal of Econometrics, Elsevier, vol. 141(2), pages 1353-1384, December.
    7. Shimotsu, Katsumi & Phillips, Peter C B, 2002. "Exact Local Whittle Estimation of Fractional Integration," Economics Discussion Papers 8838, University of Essex, Department of Economics.
    8. Tanaka, Katsuto, 1999. "The Nonstationary Fractional Unit Root," Econometric Theory, Cambridge University Press, vol. 15(4), pages 549-582, August.
    9. Faÿ, Gilles & Moulines, Eric & Roueff, François & Taqqu, Murad S., 2009. "Estimators of long-memory: Fourier versus wavelets," Journal of Econometrics, Elsevier, vol. 151(2), pages 159-177, August.
    10. Katsumi Shimotsu & Peter C.B. Phillips, 2000. "Local Whittle Estimation in Nonstationary and Unit Root Cases," Cowles Foundation Discussion Papers 1266, Cowles Foundation for Research in Economics, Yale University, revised Sep 2003.
    11. Frank S. Nielsen, 2008. "Local polynomial Whittle estimation covering non-stationary fractional processes," CREATES Research Papers 2008-28, Department of Economics and Business Economics, Aarhus University.
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    Cited by:

    1. Heni Boubaker, 2016. "A Comparative Study of the Performance of Estimating Long-Memory Parameter Using Wavelet-Based Entropies," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 693-731, December.

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    More about this item

    Keywords

    Finite sample performance comparaison; Fourier frequency; GDP; non-stationary long memory time series; wavelet;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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