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A class of modified high-order autoregressive models with improved resolution of low-frequency cycles
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UNSPECIFIED. (2004) A class of modified high-order autoregressive models with improved resolution of low-frequency cycles. JOURNAL OF TIME SERIES ANALYSIS, 25 (2). pp. 235-250. ISSN 0143-9782
Full text not available from this repository.Abstract
We consider regularly sampled processes that have most of their spectral power at low frequencies. A simple example of such a process is used to demonstrate that the standard autoregressive (AR) model, with its order selected by an information criterion, can provide a poor approximation to the process. In particular, it can result in poor multi-step predictions. We propose instead the use of a class of pth order AR models obtained by the addition of a pre-specified pth order moving average term. We present a re-parameterization of this model and show that with a low order it can provide a very good approximation to the process and its multi-step predictions. Methods of model identification and estimation are presented, based on a transformed sample spectrum, and modified partial autocorrelations. The method is also illustrated on a real example.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > QA Mathematics |
| Journal or Publication Title: | JOURNAL OF TIME SERIES ANALYSIS |
| Publisher: | BLACKWELL PUBL LTD |
| ISSN: | 0143-9782 |
| Date: | March 2004 |
| Volume: | 25 |
| Number: | 2 |
| Number of Pages: | 16 |
| Page Range: | pp. 235-250 |
| Publication Status: | Published |
| URI: | http://wrap.warwick.ac.uk/id/eprint/8660 |
Data sourced from Thomson Reuters' Web of Knowledge
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