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Factor Models for High-Dimensional Tensor Time Series

Author

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  • Rong Chen
  • Dan Yang
  • Cun-Hui Zhang

Abstract

Large tensor (multi-dimensional array) data routinely appear nowadays in a wide range of applications, due to modern data collection capabilities. Often such observations are taken over time, forming tensor time series. In this article we present a factor model approach to the analysis of high-dimensional dynamic tensor time series and multi-category dynamic transport networks. This article presents two estimation procedures along with their theoretical properties and simulation results. We present two applications to illustrate the model and its interpretations.

Suggested Citation

  • Rong Chen & Dan Yang & Cun-Hui Zhang, 2022. "Factor Models for High-Dimensional Tensor Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(537), pages 94-116, January.
    • Handle: RePEc:taf:jnlasa:v:117:y:2022:i:537:p:94-116
    DOI: 10.1080/01621459.2021.1912757
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    Cited by:

    1. Matteo Barigozzi & Giuseppe Cavaliere & Graziano Moramarco, 2022. "Factor Network Autoregressions," Papers 2208.02925, arXiv.org, revised Feb 2024.
    2. Li, Yan & Gao, Zhigen & Huang, Wei & Guo, Jianhua, 2023. "Matrix-variate data analysis by two-way factor model with replicated observations," Statistics & Probability Letters, Elsevier, vol. 202(C).
    3. He, Yong & Kong, Xinbing & Trapani, Lorenzo & Yu, Long, 2023. "One-way or two-way factor model for matrix sequences?," Journal of Econometrics, Elsevier, vol. 235(2), pages 1981-2004.
    4. Vidal-Llana, Xenxo & Uribe, Jorge M. & Guillén, Montserrat, 2023. "European stock market volatility connectedness: The role of country and sector membership," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    5. Cheng Yu & Dong Li & Feiyu Jiang & Ke Zhu, 2023. "Matrix GARCH Model: Inference and Application," Papers 2306.05169, arXiv.org.

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