Deep Learning for Solving and Estimating Dynamic Macro-Finance Models
This provides a versatile tool for economists and financial researchers to handle complex models with large state spaces, though it appears incremental as an application of existing deep learning techniques to a specific domain.
The paper tackled the challenge of solving and estimating continuous-time general equilibrium models in financial economics by developing a deep learning methodology, demonstrating its advantages in applications like industrial dynamics and macroeconomic models with financial frictions.
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.