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Deep Learning for Solving and Estimating Dynamic Macro-finance Models

Author

Listed:
  • 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)

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.

Suggested Citation

  • Benjamin Fan & Edward Qiao & Anran Jiao & Zhouzhou Gu & Wenhao Li & Lu Lu, 2025. "Deep Learning for Solving and Estimating Dynamic Macro-finance Models," Computational Economics, Springer;Society for Computational Economics, vol. 65(6), pages 3885-3921, June.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:6:d:10.1007_s10614-024-10693-3
    DOI: 10.1007/s10614-024-10693-3
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