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ARFIMA approximation and forecasting of the limiting aggregate structure of long-memory process

✍ Scribed by K. S. Man; G. C. Tiao

Publisher
John Wiley and Sons
Year
2009
Tongue
English
Weight
160 KB
Volume
28
Category
Article
ISSN
0277-6693

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✦ Synopsis

Abstract

This article studies Man and Tiao's (2006) low‐order autoregressive fractionally integrated moving‐average (ARFIMA) approximation to Tsai and Chan's (2005b) limiting aggregate structure of the long‐memory process. In matching the autocorrelations, we demonstrate that the approximation works well, especially for larger d values. In computing autocorrelations over long lags for larger d value, using the exact formula one might encounter numerical problems. The use of the ARFIMA(0, d, d̄~1~) model provides a useful alternative to compute the autocorrelations as a really close approximation. In forecasting future aggregates, we demonstrate the close performance of using the ARFIMA(0, d, d̄~1~) model and the exact aggregate structure. In practice, this provides a justification for the use of a low‐order ARFIMA model in predicting future aggregates of long‐memory process. Copyright © 2008 John Wiley & Sons, Ltd.

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