We introduce deep equilibrium nets (DEQNs)—a deep learning‐based method to compute
approximate functional rational expectations equilibria of economic models featuring a …

We present a novel computational framework that can compute global solutions to high-
dimensional dynamic stochastic economic models on irregular state space geometries. This …

We introduce our GDSGE framework and a novel global solution method, called
simultaneous transition and policy function iterations (STPFIs), for solving dynamic …

Solving dynamic economic models that capture salient real-world heterogeneity and non-
linearity requires the approximation of high-dimensional functions. As their dimensionality …

We introduce a flexible and scalable method for solving discrete-time dynamic incentive
problems with heterogeneous agents and persistent types. Our framework entails a generic …

We consider dynamic stochastic economies with heterogeneous agents and introduce the
concept of uniformly self-justified equilibria (USJE)—temporary equilibria for which …

We propose a massively parallelized and optimized framework to solve high-dimensional
dynamic stochastic economic models on modern GPU-and KNL-based clusters. First, we …

This paper introduces the concept of``self-justified equilibria" as a tractable alternative to
rational expectations equilibria in stochastic general equilibrium models with heterogeneous …

We introduce a novel numerical framework for pricing American options in high dimensions.
Our scheme manages to alleviate the problem of dimension scaling through the use of …

In this paper, we show the existence of the perfect foresight forecast functions which
generate unique and stable equilibrium price dynamics for comparative static analysis and …