Deep Learning for Solving and Estimating Dynamic Macro-Finance Models

Benjamin Fan, Edward Qiao, Anran Jiao, Zhouzhou Gu, Wenhao Li and Lu Lu

Papers from arXiv.org

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.

Date: 2023-05
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
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