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The Empirical Properties of Some Popular Estimators of Long Memory Processes

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Author Info

  • Jennifer Brown
  • Les Oxley

    () (University of Canterbury)

  • William Rea
  • Marco Reale

Abstract

We present the results of a simulation study into the properties of 12 different estimators of the Hurst parameter, H, or the fractional integration parameter, d, in long memory time series. We compare and contrast their performance on simulated Fractional Gaussian Noises and fractionally integrated series with lengths between 100 and 10,000 data points and H values between 0.55 and 0.90 or d values between 0.05 and 0.40. We apply all 12 estimators to the Campito Mountain data and estimate the accuracy of their estimates using the Beran goodness of t test for long memory time series.

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File URL: http://www.econ.canterbury.ac.nz/RePEc/cbt/econwp/0813.pdf
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Bibliographic Info

Paper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 08/13.

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Length: 17 pages
Date of creation: 26 Jun 2008
Date of revision:
Handle: RePEc:cbt:econwp:08/13

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Related research

Keywords: Strong dependence; global dependence; long range dependence; Hurst parameter estimators;

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References

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  1. Jensen, Mark J, 1999. "Using wavelets to obtain a consistent ordinary least squares estimator of the long-memory parameter," MPRA Paper 39152, University Library of Munich, Germany.
  2. Lobato, I. & Robinson, P. M., 1996. "Averaged periodogram estimation of long memory," Journal of Econometrics, Elsevier, vol. 73(1), pages 303-324, July.
  3. Baillie, Richard T. & Chung, Sang-Kuck, 2002. "Modeling and forecasting from trend-stationary long memory models with applications to climatology," International Journal of Forecasting, Elsevier, vol. 18(2), pages 215-226.
  4. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
  5. Deo, Rohit S. & Chen, Willa W., 2000. "On the integral of the squared periodogram," Stochastic Processes and their Applications, Elsevier, vol. 85(1), pages 159-176, January.
  6. Giraitis, Liudas & Robinson, Peter M. & Surgailis, Donatas, 1999. "Variance-type estimation of long memory," Stochastic Processes and their Applications, Elsevier, vol. 80(1), pages 1-24, March.
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Cited by:
  1. David Greasley & Les Oxley, 2010. "Cliometrics And Time Series Econometrics: Some Theory And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 24(5), pages 970-1042, December.
  2. Les Oxley & Chris Price & William Rea & Marco Reale, 2008. "A New Procedure to Test for H Self-Similarity," Working Papers in Economics 08/16, University of Canterbury, Department of Economics and Finance.

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