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Dynamic Mode Decompositions and Vector Autoregressions

Thomas J. Sargent et al.

International Economic Review2026https://doi.org/10.1111/iere.70068article
AJG 4ABDC A*
Weight
0.50

Abstract

We establish connections between dynamic mode decompositions (DMDs), vector autoregressions, and linear state‐space models, showing that DMD provides a computationally efficient, SVD‐based estimator of low‐rank first‐order VAR projection coefficients in high‐dimensional settings. When the measurement matrix has full column rank, the recovered nonzero eigenvalues coincide with those of the underlying state transition matrix. We apply DMD to a 100‐household heterogeneous‐agent economy with complete markets and Gorman aggregation. From high‐dimensional household income and consumption panels, DMD recovers latent aggregate dynamics, and cross‐sectional loadings reveal the sharing rule governing redistribution, demonstrating DMD's capacity to extract economically meaningful structure from microeconomic panels.

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https://doi.org/https://doi.org/10.1111/iere.70068

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@article{thomas2026,
  title        = {{Dynamic Mode Decompositions and Vector Autoregressions}},
  author       = {Thomas J. Sargent et al.},
  journal      = {International Economic Review},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/iere.70068},
}

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Dynamic Mode Decompositions and Vector Autoregressions

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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