Deep Learning for Solving and Estimating Dynamic Macro-Finance Models
Benjamin Fan Edward Qiao Anran Jiao Zhouzhou Gu Wenhao Li Lu Lu
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.