Free Access
Issue
ESAIM: PS
Volume 13, January 2009
Page(s) 115 - 134
DOI https://doi.org/10.1051/ps:2008015
Published online 26 March 2009
  1. J. Barkoulas and C.F. Baum, Long-memory forecasting of US monetary indices. J. Forecast. 25 (2006) 291–302. [CrossRef] [MathSciNet] [Google Scholar]
  2. R.J. Bhansali, Linear prediction by autoregressive model fitting in the time domain. Ann. Stat. 6 (1978) 224–231. [CrossRef] [Google Scholar]
  3. R.J. Bhansali and P.S. Kokoszka, Prediction of long-memory time series: An overview. Estadística 53 No. 160–161 (2001) 41–96. [Google Scholar]
  4. N.H. Bingham, C.M. Goldie and J.L. Teugels, Regular variation (1987). [Google Scholar]
  5. P.J. Brockwell and R.A. Davis, Simple consistent estimation of the coefficients of a linear filter. Stochastic Process. Appl. (1988) 47–59. [Google Scholar]
  6. P.J. Brockwell and R.A. Davis, Time Series: Theory and Methods. Springer Series in Statistics (1991). [Google Scholar]
  7. N. Crato and B.K. Ray, Model selection and forecasting for long-range dependent processes. J. Forecast. 15 (1996) 107–125. [CrossRef] [Google Scholar]
  8. C.W.J. Granger and R. Joyeux, An introduction to long-memory time series models and fractional differencing. J. Time Ser. Anal. 1 (1980) 15–29. [CrossRef] [MathSciNet] [Google Scholar]
  9. H.L. Gray, N.-F. Zhang and W.A. Woodward, On generalized fractional processes. J. Time Ser. Anal. 10 (1989) 233–257. [CrossRef] [MathSciNet] [Google Scholar]
  10. J.R.M. Hosking, Fractional differencing. Biometrika 68 (1981) 165–176. [CrossRef] [MathSciNet] [Google Scholar]
  11. A. Inoue, Regularly varying correlation functions and KMO-Langevin equations. Hokkaido Math. J. 26 (1997) 457–482. [MathSciNet] [Google Scholar]
  12. A. Inoue, Asymptotics for the partial autocorrelation function of a stationary process. J. Anal. Math. 81 (2000) 65–109. [CrossRef] [MathSciNet] [Google Scholar]
  13. R. Lewis and G.C. Reinsel, Prediction of multivariate time series by autoregressive model fitting. J. Multivariate Anal. 16 (1985) 393–411. [CrossRef] [MathSciNet] [Google Scholar]
  14. B. Mandelbrot and J.R. Wallis, Some long-run properties of geophysical records. Water Resour. Res. 5 (1969) 321–340. [NASA ADS] [CrossRef] [Google Scholar]
  15. M. Pourahmadi, On the convergence of finite linear predictors of stationary processes. J. Multivariate Anal. 30 (1989) 167–180. [CrossRef] [MathSciNet] [Google Scholar]
  16. B.K. Ray, Modeling long-memory processes for optimal long-range prediction. J. Time Ser. Anal. 14 (1993) 511–525. [CrossRef] [MathSciNet] [Google Scholar]
  17. L.J. Soares and L.R. Souza, Forecasting electricity demand using generalized long memory. Int. J. Forecast. 22 (2006) 17–28. [CrossRef] [Google Scholar]
  18. M.-C. Viano, Cl. Deniau and G. Oppenheim, Long-range dependence and mixing for discrete time fractional processes. J. Time Ser. Anal. 16 (1995) 323–338. [CrossRef] [MathSciNet] [Google Scholar]
  19. P. Whittle, Prediction and regulation by linear least-square methods. 2nd edn. (1963). [Google Scholar]
  20. A. Zygmund, Trigonometric series. Cambridge University Press (1968). [Google Scholar]

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