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Matrix‐Variate Time Series Analysis: A Brief Review and Some New Developments

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  • Ruey S. Tsay

Abstract

This paper briefly reviews the recent research in matrix‐variate time series analysis, discusses some new developments, especially for seasonal time series, and demonstrates some applications. A general matrix autoregressive moving‐average model is introduced. The paper narrates a simple approach for understanding the model, identifiability issues, and estimation. Real examples are used to illustrate the theory.

Suggested Citation

  • Ruey S. Tsay, 2024. "Matrix‐Variate Time Series Analysis: A Brief Review and Some New Developments," International Statistical Review, International Statistical Institute, vol. 92(2), pages 246-262, August.
    • Handle: RePEc:bla:istatr:v:92:y:2024:i:2:p:246-262
    DOI: 10.1111/insr.12558
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    References listed on IDEAS

    as
    1. Chen, Rong & Xiao, Han & Yang, Dan, 2021. "Autoregressive models for matrix-valued time series," Journal of Econometrics, Elsevier, vol. 222(1), pages 539-560.
    2. Yuefeng Han & Rong Chen & Cun-Hui Zhang & Qiwei Yao, 2021. "Simultaneous Decorrelation of Matrix Time Series," Papers 2103.09411, arXiv.org, revised Oct 2022.
    3. Hao Wang & Mike West, 2009. "Bayesian analysis of matrix normal graphical models," Biometrika, Biometrika Trust, vol. 96(4), pages 821-834.
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