Deep Learning for Solving and Estimating Dynamic Macro-finance Models

Benjamin Fan, Edward Qiao, Anran Jiao, Zhouzhou Gu, Wenhao Li and Lu Lu ()
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Benjamin Fan: Massachusetts Institute of Technology
Edward Qiao: Massachusetts Institute of Technology
Anran Jiao: Yale University
Zhouzhou Gu: Princeton University
Wenhao Li: University of Southern California
Lu Lu: Yale University

Computational Economics, 2025, vol. 65, issue 6, No 27, 3885-3921

Abstract: Abstract We develop a methodology that utilizes deep learning to simultaneously solve and estimate canonical continuous-time general equilibrium models in financial economics. We illustrate our method in two examples: (1) industrial dynamics of firms and (2) macroeconomic models with financial frictions. Through these applications, we illustrate the advantages of our method: generality, simultaneous solution and estimation, leveraging the state-of-art machine-learning techniques, and handling large state space. The method is versatile and can be applied to a vast variety of problems.

Keywords: Dynamic macro-finance models; Industrial dynamics of firms; Macroeconomic models with financial frictions; Partial differential equations; Deep learning; Parameter estimation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10693-3

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