Events

Mr. Yucheng Yang
PhD Candidate
Princeton University

We propose an efficient, reliable, and interpretable global solution method, the Deep
learning-based algorithm for Heterogeneous Agent Models (DeepHAM), 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 normative studies. As a new application, we study
constrained efficiency in heterogeneous agent models with aggregate shocks. We find
that in the presence of aggregate risk, a utilitarian planner would raise aggregate
capital for redistribution less than in absence of it because poor households do more
precautionary savings and thus rely less on labor income.