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A class of modified high order autoregressive models with improved resolution of low frequency cycles.

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.

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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: 17 Sep 2013 08:19
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/2449

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