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Multi-step forecasting for long-memory processes
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DOI：10.1002/(SICI)1099-131X(199901)18:1<59::AID-FOR711>3.0.CO;2-V.png)
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Abstract
En 中文
In this paper we present results of a simulation study to assess and compare the accuracy of forecasting techniques for long-memory processes in small sample sizes. We analyse differences between adaptive ARMA(1,1) L-step forecasts, where the parameters are estimated by minimizing the sum of squares of L-step forecast errors, and forecasts obtained by using long-memory models. We compare widths of the forecast intervals for both methods, and discuss some computational issues associated with the ARMA(1,1) method. Our results illustrate the importance and usefulness of long-memory models for multi-step forecasting. Copyright (C) 1999 John Wiley & Sons, Ltd.
Keywords:
fractionally integrated noise
long-term forecasting
ARMA (1,1)
ARFIMA
Yule-Walker
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