Dynamic Mode Decompositions and Vector Autoregressions

• 成果类型: Article
• 署名作者: Sargent, Thomas J.; Selvakumar, Yatheesan J.; Yang, Ziyue
• 署名单位: New York University; Australian National University
• 刊物名称: INTERNATIONAL ECONOMIC REVIEW
• ISSN/ISSBN: 0020-6598
• DOI: 10.1111/iere.70068
• 发表日期: 2026
• 关键词: PRINCIPAL COMPONENTS; approximation

摘要

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.