Deep Learning for Solving and Estimating Dynamic Macro-Finance Models
Computational Finance
2023-05-18 v1 Computational Engineering, Finance, and Science
Machine Learning
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.
Keywords
Cite
@article{arxiv.2305.09783,
title = {Deep Learning for Solving and Estimating Dynamic Macro-Finance Models},
author = {Benjamin Fan and Edward Qiao and Anran Jiao and Zhouzhou Gu and Wenhao Li and Lu Lu},
journal= {arXiv preprint arXiv:2305.09783},
year = {2023}
}