IDENTIFYING MULTIVARIATE TIME SERIES MODELS

D. M. Cooper, Eric F. Wood

Research output: Contribution to journalArticlepeer-review

21 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 languageEnglish (US)
Pages (from-to)153-164
Number of pages12
JournalJournal of Time Series Analysis
Volume3
Issue number3
DOIs
StatePublished - 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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