We introduce a unified deep learning method that solves dynamic economic models by
casting them into nonlinear regression equations. We derive such equations for three …

We review the current state of the estimation of dynamic stochastic general equilibrium
(DSGE) models. After introducing a general framework for dealing with DSGE models, the …

We postulate a continuous‐time heterogeneous agent model with a financial sector and
households to study the nonlinear linkages between aggregate and financial variables. In …

We propose a new method for solving high-dimensional dynamic programming problems
and recursive competitive equilibria with a large (but finite) number of heterogeneous agents …

This paper proposes an analytic representation of perturbations in heterogeneous agent
economies with aggregate shocks up to any order. Treating the underlying distribution as an …

We develop a machine-learning solution algorithm to solve for optimal portfolio choice in a
lifecycle model that includes many features of reality modelled only separately in previous …

To answer a wide range of important economic questions, researchers must solve
highdimensional dynamic programming problems. This is particularly true in models …

To analyse climate change mitigation strategies, economists rely on simplified climate
models—so-called climate emulators—that provide a realistic quantitative link between CO …

An efficient, reliable, and interpretable global solution method, the Deep learning-based
algorithm for Heterogeneous Agent Models (DeepHAM), is proposed for solving high …

We study the implications of model uncertainty in a climate-economics framework with three
types of capital:" dirty" capital that produces carbon emissions when used for production," …