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Deep Learning for Solving and Estimating Dynamic Macro-Finance Models

Author

Listed:
  • Benjamin Fan
  • Edward Qiao
  • Anran Jiao
  • Zhouzhou Gu
  • Wenhao Li
  • Lu Lu

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, 2023. "Deep Learning for Solving and Estimating Dynamic Macro-Finance Models," Papers 2305.09783, arXiv.org.
    • Handle: RePEc:arx:papers:2305.09783
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    References listed on IDEAS

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