Minisymposium Presentation

We propose new global solution algorithms for continuous time heterogeneous agent economies with aggregate shocks.We first approximate the state space so the master equation becomes a high, but finite, dimensional partial differential equation. We then approximate the value function using neural networks and solve the master equation using deep learning tools. The main advantage of this technique is that it allows us to find global solutions to high dimensional, non-linear problems. We consider two broad approaches to reducing the dimensionality of the problem: discretizing the number of agents and projecting the distribution. We demonstrate our algorithms by solving two canonical models in the macroeconomics literature: the Aiyagari (1994) model and the Krusell and Smith (1998) model.