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

This paper proposes a global algorithm to solve a large class of nonlinear continuous-time
models in finance and economics. Using tools from machine learning, I recast problem of …

We develop an algorithm for solving a large class of nonlinear high-dimensional continuous-
time models in finance. We approximate value and policy functions using deep learning and …

This paper presents a dynamic stochastic general equilibrium model of Ricardian business
cycles. Our model is Ricardian because countries (or, equivalently, regions) trade to take …

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 scalable method for computing global solutions of nonlinear, high-
dimensional dynamic stochastic economic models. First, within a time iteration framework …

We analyze public debt policies within a calibrated stochastic OLG model with distortionary
taxation. The risk-free interest rate is realistically sensitive to public debt and lower than the …

This paper presents a comprehensive method for efficiently solving stochastic Integrated
Assessment Models (IAMs) and performing parametric uncertainty quantification. Our …

We propose a pseudospectral method to solve heterogeneous-agent models in continuous
time. The solution is approximated as a sum of smooth global basis functions, in our case …