Ruofan Yu, Rong Chen, Han Xiao, Yuefeng Han
arXiv 8 Jul 2024 · Statistics — Methodology · 2 citations (OpenAlex)
arXiv:2407.05624 · PDF · DOI · OpenAlex · Extracted main text
Matrix time series, which consist of matrix-valued data observed over time, are prevalent in various fields such as economics, finance, and engineering. Such matrix time series data are often observed in high dimensions. Matrix factor models are employed to reduce the dimensionality of such data, but they lack the capability to make predictions without specified dynamics in the latent factor process. To address this issue, we propose a two-component dynamic matrix factor model that extends the standard matrix factor model by incorporating a matrix autoregressive structure for the low-dimensional latent factor process. This two-component model injects prediction capability to the matrix factor model and provides deeper insights into the dynamics of high-dimensional matrix time series. We present the estimation procedures of the model and their theoretical properties, as well as empirical analysis of the estimation procedures via simulations, and a case study of New York city taxi data, demonstrating the performance and usefulness of the model.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chen, R., Xiao, H., and Yang, D (2021) Autoregressive models for matrix-valued time series self | 1.000 | 12 | 5 | 100% |
| 2 | Chen, R., Yang, D., and Zhang, C.-H (2022) Factor models for high-dimensional tensor time series self | 1.000 | 9 | 6 | 100% |
| 3 | Wang, D., Liu, X., and Chen, R (2019) Factor models for matrix-valued high-dimensional time series self | 1.000 | 8 | 3 | 100% |
| 4 | Xiao, H., Han, Y., Chen, R., and Liu, C (2023) Reduced rank autoregressive models for matrix time series self | 1.000 | 5 | 4 | 100% |
| 5 | Han, Y., Chen, R., Yang, D., and Zhang, C.-H (2020) Tensor factor model estimation by iterative projection self | 0.909 | 12 | 5 | 75% |
| 6 | Gourieroux, C. and Jasiak, J (2001) Dynamic factor models | 0.644 | 2 | 2 | 100% |
| 7 | Bai, J (2003) Inferential theory for factor models of large dimensions | 0.644 | 2 | 2 | 100% |
| 8 | Chen, E. Y. and Fan, J (2023) Statistical inference for high-dimensional matrix-variate factor models | 0.644 | 2 | 2 | 100% |
| 9 | Fan, J., Liao, Y., and Mincheva, M (2013) Large covariance estimation by thresholding principal orthogonal complements | 0.644 | 2 | 2 | 100% |
| 10 | Barigozzi, M. and Hallin, M (2016) Generalized dynamic factor models and volatilities: recovering the market volatility shocks | 0.644 | 2 | 2 | 100% |
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