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Multi-step forecasting for long-memory processes

✍ Scribed by Julia Brodsky; Clifford M. Hurvich

Publisher: John Wiley and Sons
Year: 1999
Tongue: English
Weight: 298 KB
Volume: 18
Category: Article
ISSN: 0277-6693
DOI: 10.1002/(sici)1099-131x(199901)18:1<59::aid-for711>3.0.co;2-v

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✦ Synopsis

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 dierences 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 longmemory 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.

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