A class of modified high-order autoregressive models with improved resolution of low-frequency cycles
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 0143-9782Full text not available from this repository.
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|
|Number of Pages:||16|
|Page Range:||pp. 235-250|
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