arXiv 6 Feb 2025 · Statistics — Methodology
arXiv:2502.04112 · PDF · DOI · OpenAlex · Extracted main text
This paper considers an approximate dynamic matrix factor model that accounts for the time series nature of the data by explicitly modelling the time evolution of the factors. We study estimation of the model parameters based on the Expectation Maximization (EM) algorithm, implemented jointly with the Kalman smoother which gives estimates of the factors. We establish the consistency of the estimated loadings and factor matrices as the sample size $T$ and the matrix dimensions $p_1$ and $p_2$ diverge to infinity. We then illustrate two immediate extensions of this approach to: (a) the case of arbitrary patterns of missing data and (b) the presence of common stochastic trends. The finite sample properties of the estimators are assessed through a large simulation study and two applications on: (i) a financial dataset of volatility proxies and (ii) a macroeconomic dataset covering the main euro area countries.
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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, Rong and Giannerini, Simone and Goracci, Greta and Trapani, Lo… (2025) Inference in matrix-valued time series with common stochastic trends and multifactor error structure | 1.000 | 7 | 3 | 100% |
| 2 | Doz, Catherine and Giannone, Domenico and Reichlin, Lucrezia (2012) A quasi-maximum likelihood approach for large, approximate dynamic factor models | 1.000 | 6 | 3 | 100% |
| 3 | Bańbura, Marta and Modugno, Michele (2014) Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data | 1.000 | 5 | 5 | 100% |
| 4 | Cen, Zetai and Lam, Clifford (2025) Tensor time series imputation through tensor factor modelling | 0.961 | 9 | 5 | 89% |
| 5 | Chen, Elynn Y and Fan, Jianqing (2023) Statistical Inference for High-Dimensional Matrix-Variate Factor Models | 0.950 | 7 | 5 | 86% |
| 6 | Yu, Ruofan and Chen, Rong and Xiao, Han and Han, Yuefeng (2024) Dynamic Matrix Factor Models for High Dimensional Time Series | 0.928 | 4 | 3 | 100% |
| 7 | Yu, Long and He, Yong and Kong, Xinbing and Zhang, Xinsheng (2022) Projected estimation for large-dimensional matrix factor models | 0.737 | 30 | 6 | 40% |
| 8 | Durbin, James and Koopman, Siem Jan (2012) Time Series Analysis by State Space Methods | 0.737 | 3 | 2 | 100% |
| 9 | Chen, Rong and Xiao, Han and Yang, Dan (2021) Autoregressive models for matrix-valued time series | 0.644 | 3 | 2 | 67% |
| 10 | Bai, Jushan (2004) Estimating cross-section common stochastic trends in nonstationary panel data | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 46 scored citations.