Bin Chen, Yuefeng Han, Qiyang Yu
arXiv 25 Jun 2024 · Statistics — Methodology · 4 citations (OpenAlex)
arXiv:2406.17278 · PDF · DOI · OpenAlex · Extracted main text
High-dimensional tensor-valued data have recently gained attention from researchers in economics and finance. We consider the estimation and inference of high-dimensional tensor factor models, where each dimension of the tensor diverges. Our focus is on a factor model that admits CP-type tensor decomposition, which allows for non-orthogonal loading vectors. Based on the contemporary covariance matrix, we propose an iterative simultaneous projection estimation method. Our estimator is robust to weak dependence among factors and weak correlation across different dimensions in the idiosyncratic shocks. We establish an inferential theory, demonstrating both consistency and asymptotic normality under relaxed assumptions. Within a unified framework, we consider two eigenvalue ratio-based estimators for the number of factors in a tensor factor model and justify their consistency. Simulation studies confirm the theoretical results and an empirical application to sorted portfolios reveals three important factors: a market factor, a long-short factor, and a volatility factor.
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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 | Babii, A., Ghysels, E., and Pan, J (2022) Tensor principal component analysis | 0.965 | 10 | 6 | 90% |
| 2 | Han, Y., Yang, D., Zhang, C.-H., and Chen, R (2024) Cp factor model for dynamic tensors self | 0.965 | 10 | 5 | 90% |
| 3 | Chang, J., He, J., Yang, L., and Yao, Q (2023) Modelling matrix time series via a tensor CP-decomposition | 0.941 | 6 | 4 | 83% |
| 4 | Kolda, T. G. and Bader, B. W (2009) Tensor decompositions and applications | 0.928 | 5 | 3 | 80% |
| 5 | Lettau, M (2024) 3d-pca: Factor models with restrictions | 0.909 | 8 | 5 | 75% |
| 6 | Bai, J (2003) Inferential theory for factor models of large dimensions | 0.874 | 7 | 2 | 100% |
| 7 | Chen, E. Y. and Fan, J (2023) Statistical inference for high-dimensional matrix-variate factor models | 0.843 | 4 | 3 | 75% |
| 8 | Han, Y., Chen, R., Yang, D., and Zhang, C.-H (2022) Tensor factor model estimation by iterative projection self | 0.843 | 3 | 3 | 100% |
| 9 | Anandkumar, A., Ge, R., and Janzamin, M (2014) Guaranteed non-orthogonal tensor decomposition via alternating rank-1 updates | 0.843 | 3 | 3 | 100% |
| 10 | Bai, J. and Ng, S (2002) Determining the number of factors in approximate factor models | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 54 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Modewise Additive Factor Model for Matrix Time Series | 1.000 | 5 | 3 |
| 2 | CP-Factorization for High Dimensional Tensor Time Series and Double Projection Iterations | 1.000 | 5 | 3 |
| 3 | Threshold Tensor Factor Model in CP Form | 0.928 | 5 | 3 |
| 4 | Diffusion Index Forecasting with Tensor Data | 0.914 | 17 | 5 |
| 5 | Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models | 0.405 | 1 | 1 |