Alain Hecq, Ivan Ricardo, Ines Wilms
arXiv 24 Sep 2025 · Econometrics · publishedEconometrics and Statistics (2026)
arXiv:2509.19911 · PDF · DOI · OpenAlex · Extracted main text
We propose a pseudo-structural framework for analyzing contemporaneous co-movements in reduced-rank matrix autoregressive (RRMAR) models. Unlike conventional vector-autoregressive (VAR) models that would discard the matrix structure, our formulation preserves it, enabling a decomposition of co-movements into three interpretable components: row-specific, column-specific, and joint (row-column) interactions across the matrix-valued time series. Our estimator admits standard asymptotic inference and we propose a BIC-type criterion for the joint selection of the reduced ranks and the autoregressive lag order. We validate the method's finite-sample performance in terms of estimation accuracy, coverage and rank selection in simulation experiments, including cases of rank misspecification. We illustrate the method's practical usefelness in identifying co-movement structures in two empirical applications: U.S. state-level coincident and leading indicators, and cross-country macroeconomic indicators.
appendix boundary found by appendix_command · 86% of the source is main text. Read the extracted text to check this.
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 | Xiao, H.; Han, Y.; Chen, R. and Liu, C. (Forthcoming), Reduced rank… | 0.935 | 11 | 4 | 82% |
| 2 | Engle, R. F. and Kozicki, S (1993) Testing for common features | 0.737 | 3 | 2 | 100% |
| 3 | Chen, R.; Xiao, H. and Yang, D (2021) Autoregressive models for matrix-valued time series | 0.644 | 2 | 2 | 100% |
| 4 | Lütkepohl, H (2005) New introduction to multiple time series analysis | 0.511 | 2 | 2 | 50% |
| 5 | Akaike, H (1974) A new look at the statistical model identification | 0.405 | 1 | 1 | 100% |
| 6 | Chen, E. Y. and Fan, J (2023) Statistical inference for high-dimensional matrix-variate factor models | 0.405 | 1 | 1 | 100% |
| 7 | Chen, R.; Giannerini, S.; Goracci, G. and Trapani, L (2025) Inference in matrix-valued time series with common stochastic trends and multifactor error structure | 0.405 | 1 | 1 | 100% |
| 8 | Chen, R.; Yang, D. and Zhang, C.-H (2022) Factor models for high-dimensional tensor time series | 0.405 | 1 | 1 | 100% |
| 9 | Cubadda, G. and Hecq, A (2001) On non-contemporaneous short-run co-movements self | 0.405 | 1 | 1 | 100% |
| 10 | Cubadda, G. and Hecq, A (2022) a), Dimension reduction for high-dimensional vector autoregressive models self | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 34 scored citations.