  An efficient, reliable, and interpretable global solution method, the Deep
learning-based algorithm for Heterogeneous Agent Models (DeepHAM), is proposed
for solving high dimensional heterogeneous agent models with aggregate shocks.
The state distribution is approximately represented by a set of optimal
generalized moments. Deep neural networks are used to approximate the value and
policy functions, and the objective is optimized over directly simulated paths.
In addition to being an accurate global solver, this method has three
additional features. First, it is computationally efficient in solving complex
heterogeneous agent models, and it does not suffer from the curse of
dimensionality. Second, it provides a general and interpretable representation
of the distribution over individual states, which is crucial in addressing the
classical question of whether and how heterogeneity matters in macroeconomics.
Third, it solves the constrained efficiency problem as easily as it solves the
competitive equilibrium, which opens up new possibilities for studying optimal
monetary and fiscal policies in heterogeneous agent models with aggregate
shocks.
