Tunnicliffe Wilson, Granville and Morton, Alex S. (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.
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: | Article |
|---|---|
| Journal or Publication Title: | Journal of Time Series Analysis |
| Additional Information: | RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research |
| Subjects: | Q Science > QA Mathematics |
| Departments: | Faculty of Science and Technology > Mathematics and Statistics |
| ID Code: | 2449 |
| Deposited By: | ep_importer |
| Deposited On: | 29 Mar 2008 16:51 |
| Refereed?: | Yes |
| Published?: | Published |
| Last Modified: | 26 Jul 2012 16:25 |
| Identification Number: | |
| URI: | http://eprints.lancs.ac.uk/id/eprint/2449 |
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