Department of Industial Engineering & Decision Analysis [IEDA / MATH Joint Seminar] - DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate Shocks

2:00pm - 3:15pm
Room 3598 (lift 27-28)

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 studying optimal monetary and fiscal policies in heterogeneous agent models with aggregate shocks.


( working paper : )

講者/ 表演者:
Dr. Jiequn Han
Center for Computational Mathematics, Flatiron Institute

Dr. Han Jiequn is currently a Flatiron Research Fellow at the Center for Computational Mathematics, Flatiron Institute. Dr. Han obtained his Ph.D. degree in applied mathematics from the Program in Applied and Computational Mathematics (PACM), Princeton University in June 2018, advised by Prof. Weinan E. Dr. Han’s research draws inspiration from various disciplines of science and is devoted to solving high-dimensional problems arising from scientific computing. His current research interests mainly focus on solving high-dimensional partial differential equations and machine learning based-multiscale modelling.

Department of Industrial Engineering & Decision Analytics