Zhiren Ma, Qian Zhao, Riquan Zhang, Zhaoxing Gao
arXiv 6 Aug 2025 · Econometrics
arXiv:2508.04259 · PDF · Extracted main text
This paper proposes a novel diffusion-index model for forecasting when predictors are high-dimensional matrix-valued time series. We apply an $\alpha$-PCA method to extract low-dimensional matrix factors and build a bilinear regression linking future outcomes to these factors, estimated via iterative least squares. To handle weak factor structures, we introduce a supervised screening step to select informative rows and columns. Theoretical properties, including consistency and asymptotic normality, are established. Simulations and real data show that our method significantly improves forecast accuracy, with the screening procedure providing additional gains over standard benchmarks in out-of-sample mean squared forecast error.
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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 and Fan (2023) Statistical inference for high-dimensional matrix-variate factor models | 1.000 | 12 | 5 | 100% |
| 2 | Chen, Xiao, and Yang (2021) Autoregressive models for matrix-valued time series | 1.000 | 8 | 4 | 100% |
| 3 | Lam and Yao (2012) Factor modeling for high-dimensional time series: inference for the number of factors | 0.874 | 5 | 2 | 100% |
| 4 | Ahn and Horenstein (2013) Eigenvalue ratio test for the number of factors | 0.644 | 2 | 2 | 100% |
| 5 | Wang, Liu, and Chen (2019) Factor models for matrix-valued high-dimensional time series | 0.644 | 2 | 2 | 100% |
| 6 | Chang, Guo, and Yao (2015) High dimensional stochastic regression with latent factors, endogeneity and nonlinearity | 0.511 | 2 | 1 | 100% |
| 7 | Lam, Yao, and Bathia (2011) Estimation of latent factors for high-dimensional time series | 0.511 | 2 | 1 | 100% |
| 8 | Stock and Watson (2005) An empirical comparison of methods for forecasting using many predictors | 0.511 | 2 | 1 | 100% |
| 9 | Stock and Watson (2002) Macroeconomic Forecasting Using Diffusion Indexes | 0.405 | 1 | 1 | 100% |
| 10 | Stock and Watson (2002) Forecasting Using Principal Components From a Large Number of Predictors | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 58 scored citations.