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Identifying Multivariate Time Series Models

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  • Ruey S. Tsay

Abstract

. This paper is concerned with how canonical variate analysis can be used to identify the structure of a linear multivariate time series model. The procedure used is based on that of Akaike and Cooper and Wood. A correction and a refinement are made, however. The correction is on the testing statistic and the refinement on the allowed order (p, q). Appropriate asymptotic distributions for testing zero canonical correlations are also given.

Suggested Citation

  • Ruey S. Tsay, 1989. "Identifying Multivariate Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(4), pages 357-372, July.
    • Handle: RePEc:bla:jtsera:v:10:y:1989:i:4:p:357-372
    DOI: 10.1111/j.1467-9892.1989.tb00034.x
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    Cited by:

    1. Alfredo García-Hiernaux & José Casals & Miguel Jerez, 2012. "Estimating the system order by subspace methods," Computational Statistics, Springer, vol. 27(3), pages 411-425, September.
    2. Melard, Guy & Roy, Roch & Saidi, Abdessamad, 2006. "Exact maximum likelihood estimation of structured or unit root multivariate time series models," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 2958-2986, July.
    3. Bhansali, Rajendra J., 2020. "Model specification and selection for multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 175(C).
    4. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
    5. Yu‐Pin Hu & Rouh‐Jane Chou, 2004. "On The Peña–Box Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 811-830, November.

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