Alain Hecq, Ivan Ricardo, Ines Wilms
arXiv 10 Jul 2024 · Econometrics · 2 citations (OpenAlex)
arXiv:2407.07973 · PDF · DOI · OpenAlex · Extracted main text
Reduced-rank regressions are powerful tools used to identify co-movements within economic time series. However, this task becomes challenging when we observe matrix-valued time series, where each dimension may have a different co-movement structure. We propose reduced-rank regressions with a tensor structure for the coefficient matrix to provide new insights into co-movements within and between the dimensions of matrix-valued time series. Moreover, we relate the co-movement structures to two commonly used reduced-rank models, namely the serial correlation common feature and the index model. Two empirical applications involving U.S.\ states and economic indicators for the Eurozone and North American countries illustrate how our new tools identify co-movements.
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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 | Wang, D.; Zheng, Y. and Li, G (2024) High-dimensional low-rank tensor autoregressive time series modeling | 1.000 | 10 | 3 | 100% |
| 2 | Wang, D.; Zheng, Y.; Lian, H. and Li, G (2022) b), High-dimensional vector autoregressive time series modeling via tensor decomposition | 1.000 | 5 | 3 | 100% |
| 3 | Cubadda, G. and Hecq, A (2022) a), Dimension reduction for high-dimensional vector autoregressive models self | 0.874 | 5 | 2 | 100% |
| 4 | Lam, C. and Yao, Q (2012) Factor modeling for high-dimensional time series: Inference for the number of factors | 0.811 | 4 | 2 | 100% |
| 5 | Xiao, H.; Han, Y.; Chen, R. and Liu, C (2022) Reduced rank autoregressive models for matrix time series | 0.811 | 4 | 2 | 100% |
| 6 | Carriero, A.; Kapetanios, G. and Marcellino, M (2016) Structural analysis with multivariate autoregressive index models | 0.737 | 3 | 2 | 100% |
| 7 | Chen, R.; Xiao, H. and Yang, D (2021) Autoregressive models for matrix-valued time series | 0.737 | 3 | 2 | 100% |
| 8 | Engle, R. F. and Kozicki, S (1993) Testing for common features | 0.737 | 3 | 2 | 100% |
| 9 | Billio, M.; Casarin, R.; Iacopini, M. and Kaufmann, S (2023) Bayesian dynamic tensor regression | 0.644 | 2 | 2 | 100% |
| 10 | Velu, R. P.; Reinsel, G. C. and Wichern, D. W (1986) Reduced rank models for multiple time series | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 52 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Detecting Cointegrating Relations in Non-stationary Matrix-Valued Time Series | 0.405 | 1 | 1 |
| 2 | Decomposing Co-Movements in Matrix-Valued Time Series: A Pseudo-Structural Reduced-Rank Approach | 0.405 | 1 | 1 |