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. ISSN 0143-9782

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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:
Journal Article
Journal or Publication Title:
Journal of Time Series Analysis
Additional Information:
RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research
Uncontrolled Keywords:
/dk/atira/pure/researchoutput/libraryofcongress/qa
Subjects:
ID Code:
2449
Deposited By:
Deposited On:
29 Mar 2008 16:51
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Jan 2020 06:38