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.

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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
Subjects:	Q Science > QA Mathematics
Journal or Publication Title:	JOURNAL OF TIME SERIES ANALYSIS
Publisher:	BLACKWELL PUBL LTD
ISSN:	0143-9782
Official Date:	March 2004
Dates:	Date Event March 2004 UNSPECIFIED
Volume:	25
Number:	2
Number of Pages:	16
Page Range:	pp. 235-250
Publication Status:	Published

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