Multi-step forecasting for long-memory processes

Journal of Forecasting

Volumn 18, Issue 1, 1999, Pages 59-75

  Brodsky, Julia ^a   Hurvich, Clifford M ^b,c  

^a AT AND T LABS RESEARCH   (United States)

^b NEW YORK UNIVERSITY   (United States)

^c NEW YORK UNIVERSITY   (United States)

Author keywords

ARFIMA; ARMA (1,1); Fractionally integrated noise; Long term forecasting; Yule Walker

Indexed keywords

EID: 0002409177     PISSN: 02776693     EISSN: None     Source Type: Journal    
DOI: 10.1002/(SICI)1099-131X(199901)18:1<59::AID-FOR711>3.0.CO;2-V     Document Type: Article

References (6)

1. Beran, J., 'Statistics for long-memory processes', Monographs on statistics and applied probability 61, New York: Chapman & Hall, 220-223.
2. Box, G. E. P. and Jenkins, G. M., Time-series Analysis: Forecasting and Control, San Francisco: Holden-Day.
3. Crato, N. and Ray, B., 'Model selection and forecasting for long-range dependent processes', J. Forecasting, 15 (1996), 107-125.
4. Davies, R. B. and Harte, D. S., 'Tests for Hurst effect', Biometrika, 74 (1987), 95-102.
5. Haslett, J. and Raftery, A. E., 'Space-time modeling with long-memory dependence: assessing Ireland's wind power resources (with discussion)', J. Roy. Statistic. Soc. Ser. C, 38 (1989), 1-21.
6. Tiao, G. C. and Tsay, R. S., 'Some advances in non-linear and adaptive modeling in time-series', J. Forecasting, 13 (1994), 109-131.