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Bilateral matrix spatiotemporal autoregressive model

Author

Listed:
  • Qin, Lei
  • Zhang, Xiaomei
  • Zhu, Yingqiu
  • Chen, Yang
  • Shia, Ben-Chang

Abstract

As time series with matrix structures becoming more and more common in the fields of finance, economics, and management, modeling matrix-valued time series becomes an emerging research hotspot. Spatial effects lead by different locations play an important role in the analysis of time series. Although matrix autoregressive model (MAR) provides a promising solution for modeling matrix-valued time series, it only models the dynamic effects in the temporal dimension, without capturing the spatial effects. In this paper, we propose a bilateral matrix spatiotemporal autoregressive model (BMSAR), which fully considers the pure spatial effects, pure dynamic effects, and time-delay spatial effects while maintaining and utilizing the matrix structure. In order to solve the endogeneity problem, the estimation process for BMSAR is based on the least squares method and the Yule-Walker equation for iterative estimation. The simulation results show that as compared with the MAR, the BMSAR model effectively reflects the impact of spatial structure on the sequence observations. The estimator for BMSAR proposed in this paper is consistent. It achieves promising performance when the sample size is relatively large. The proposed model and algorithm are also verified using the trade and macroeconomic indicator datasets of seven countries in the G7 summit, and the prediction accuracy is significantly improved as compared with the existing models.

Suggested Citation

  • Qin, Lei & Zhang, Xiaomei & Zhu, Yingqiu & Chen, Yang & Shia, Ben-Chang, 2026. "Bilateral matrix spatiotemporal autoregressive model," Computational Statistics & Data Analysis, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:csdana:v:215:y:2026:i:c:s0167947325001677
    DOI: 10.1016/j.csda.2025.108291
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    References listed on IDEAS

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