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Deep-MacroFin: Informed Equilibrium Neural Network for Continuous Time Economic Models

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  • Yuntao Wu
  • Jiayuan Guo
  • Goutham Gopalakrishna
  • Zissis Poulos

Abstract

In this paper, we present Deep-MacroFin, a comprehensive framework designed to solve partial differential equations, with a particular focus on models in continuous time economics. This framework leverages deep learning methodologies, including Multi-Layer Perceptrons and the newly developed Kolmogorov-Arnold Networks. It is optimized using economic information encapsulated by Hamilton-Jacobi-Bellman (HJB) equations and coupled algebraic equations. The application of neural networks holds the promise of accurately resolving high-dimensional problems with fewer computational demands and limitations compared to other numerical methods. This framework can be readily adapted for systems of partial differential equations in high dimensions. Importantly, it offers a more efficient (5$\times$ less CUDA memory and 40$\times$ fewer FLOPs in 100D problems) and user-friendly implementation than existing libraries. We also incorporate a time-stepping scheme to enhance training stability for nonlinear HJB equations, enabling the solution of 50D economic models.

Suggested Citation

  • Yuntao Wu & Jiayuan Guo & Goutham Gopalakrishna & Zissis Poulos, 2024. "Deep-MacroFin: Informed Equilibrium Neural Network for Continuous Time Economic Models," Papers 2408.10368, arXiv.org, revised May 2025.
    • Handle: RePEc:arx:papers:2408.10368
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    References listed on IDEAS

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    1. Larry G. Epstein & Stanley E. Zin, 2013. "Substitution, risk aversion and the temporal behavior of consumption and asset returns: A theoretical framework," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 12, pages 207-239, World Scientific Publishing Co. Pte. Ltd..
    2. Goutham Gopalakrishna, 2021. "ALIENs and Continuous Time Economies," Swiss Finance Institute Research Paper Series 21-34, Swiss Finance Institute.
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