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A comparison of techniques of estimation in long-memory processes

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  • Bisaglia, Luisa
  • Guegan, Dominique

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File URL: http://www.sciencedirect.com/science/article/B6V8V-3SX829Y-F/2/35d32902fe231fa3bebc7a764b07d142
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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 27 (1998)
Issue (Month): 1 (March)
Pages: 61-81

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Handle: RePEc:eee:csdana:v:27:y:1998:i:1:p:61-81

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Web page: http://www.elsevier.com/locate/csda

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References

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  1. Mandelbrot, Benoit, 1969. "Long-Run Linearity, Locally Gaussian Process, H-Spectra and Infinite Variances," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 10(1), pages 82-111, February.
  2. Hauser, Michael A. & Reschenhofer, Erhard, 1995. "Estimation of the fractionally differencing parameter with the R/S method," Computational Statistics & Data Analysis, Elsevier, vol. 20(5), pages 569-579, November.
  3. Andrew W. Lo, 1989. "Long-term Memory in Stock Market Prices," NBER Working Papers 2984, National Bureau of Economic Research, Inc.
  4. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
  5. B. Mandelbrot, 1972. "Statistical Methodology For Nonperiodic Cycles: From The Covariance To Rs Analysis," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 1, number 3, pages 259-290 National Bureau of Economic Research, Inc.
  6. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
  7. Granger, Clive W. J. & Ding, Zhuanxin, 1996. "Varieties of long memory models," Journal of Econometrics, Elsevier, vol. 73(1), pages 61-77, July.
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Citations

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Cited by:
  1. Leonardo Rocha Souza, 2007. "Temporal Aggregation and Bandwidth selection in estimating long memory," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(5), pages 701-722, 09.
  2. Leonardo Souza & Jeremy Smith & Reinaldo Souza, 2006. "Convex combinations of long memory estimates from different sampling rates," Computational Statistics, Springer, vol. 21(3), pages 399-413, December.
  3. Franco, Glaura C. & Reisen, Valderio A., 2007. "Bootstrap approaches and confidence intervals for stationary and non-stationary long-range dependence processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 546-562.
  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. 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.
  7. Souza, Leonardo R. & Smith, Jeremy, 2002. "Bias in the memory parameter for different sampling rates," International Journal of Forecasting, Elsevier, vol. 18(2), pages 299-313.
  8. Javier Contreras-Reyes & Wilfredo Palma, 2013. "Statistical analysis of autoregressive fractionally integrated moving average models in R," Computational Statistics, Springer, vol. 28(5), pages 2309-2331, October.
  9. 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.

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