ARFIMA approximation and forecasting of the limiting aggregate structure of long-memory process
AbstractThis 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 , &dmacr; 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 , &dmacr; 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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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.
Volume (Year): 28 (2009)
Issue (Month): 2 ()
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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966
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