We develop numerically stable and accurate stochastic simulation approaches for solving
dynamic economic models. First, instead of standard least‐squares approximation methods …

We survey numerical methods that are tractable in dynamic economic models with a finite,
large number of continuous state variables.(Examples of such models are new Keynesian …

This paper studies the properties of the solution to the heterogeneous agents model in Den
Haan et al.[2009. Computational suite of models with heterogeneous agents: incomplete …

Abstract Standard optimal Debt Management (DM) models prescribe a dominant role for
long bonds and advocate against issuing short bonds. They require very large positions in …

We argue that deep learning provides a promising avenue for taming the curse of
dimensionality in quantitative economics. We begin by exploring the unique challenges …

We use the stochastic simulation algorithm, described in Judd et al.(2009), and the cluster-
grid algorithm, developed in Judd et al.(2010a), to solve a collection of multi-country real …

This paper develops a global solution method to solve large state space macro-finance
models using machine learning. Our new method, an artificial neural network expectation …

The analysis of dynamic economic models routinely leads to the mathematical problem of
determining an unknown function for which no closed-form solution exists. Economists must …

We use supervised machine learning to approximate the expectations typically contained in
the optimality conditions of an economic model in the spirit of the parameterized …

This paper presents a computable general equilibrium model of endogenous (stochastic)
growth and cycles that can account for two key features of the aggregate data: balanced …