mlsanityData to 14 Oct 2026

Deep Learning for Solving and Estimating Dynamic Macro-Finance Models

arXiv 2305.09783 ↗First posted 5 May 2023q-fin.CP cs.CE cs.LGcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 9 of its 62 references that are in the map. First posted on arXiv 5 May 2023.

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9 of 62 references.

References · 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 1.

6 papers.

  1. 2024Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestrationcited by 11
  2. 2025RED-DiffEq: Regularization by denoising diffusion models for solving inverse PDE problems with application to full waveform inversioncited by 5
  3. 2024Deep-MacroFin: Informed Equilibrium Neural Network for Continuous Time Economic Modelscited by 1
  4. 2025DeepSVM: Learning Stochastic Volatility Models with Physics-Informed Deep Operator Networkscited by 1
  5. 2025Modeling Adoptive Cell Therapy in Bladder Cancer from Sparse Biological Data using PINNscited by 0
  6. 2026Nested Fourier-enhanced neural operator for efficient modeling of radiation transfer in firescited by 0