IDENTIFYING MULTIVARIATE TIME SERIES MODELS

D. M. Cooper; Eric F. Wood

doi:10.1111/j.1467-9892.1982.tb00337.x

IDENTIFYING MULTIVARIATE TIME SERIES MODELS

D. M. Cooper, Eric F. Wood

Research output: Contribution to journal › Article › peer-review

16 Scopus citations

Abstract

Abstract. Akaike (1974, 1975) has described how canonical variate analysis can be used to identify the structure of linear multivariate time series models. With some modification, the procedure is suitable for finding autoregressive moving average representations which are efficiently parameterized. We describe briefly the method and examine its performance when applied to a well‐known bivariate time series.

Original language	English (US)
Pages (from-to)	153-164
Number of pages	12
Journal	Journal of Time Series Analysis
Volume	3
Issue number	3
DOIs	https://doi.org/10.1111/j.1467-9892.1982.tb00337.x
State	Published - May 1982

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty
Applied Mathematics

Keywords

Canonical variate analysis
mink‐muskrat data

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10.1111/j.1467-9892.1982.tb00337.x

Cite this

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abstract = "Abstract. Akaike (1974, 1975) has described how canonical variate analysis can be used to identify the structure of linear multivariate time series models. With some modification, the procedure is suitable for finding autoregressive moving average representations which are efficiently parameterized. We describe briefly the method and examine its performance when applied to a well‐known bivariate time series.",

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AU - Wood, Eric F.

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N2 - Abstract. Akaike (1974, 1975) has described how canonical variate analysis can be used to identify the structure of linear multivariate time series models. With some modification, the procedure is suitable for finding autoregressive moving average representations which are efficiently parameterized. We describe briefly the method and examine its performance when applied to a well‐known bivariate time series.

AB - Abstract. Akaike (1974, 1975) has described how canonical variate analysis can be used to identify the structure of linear multivariate time series models. With some modification, the procedure is suitable for finding autoregressive moving average representations which are efficiently parameterized. We describe briefly the method and examine its performance when applied to a well‐known bivariate time series.

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KW - mink‐muskrat data

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ER -

IDENTIFYING MULTIVARIATE TIME SERIES MODELS

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

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