Dynamic stochastic general equilibrium (DSGE) models have become one of the
workhorses of modern macroeconomics and are extensively used for academic research as …

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 …

This chapter provides an overview of solution and estimation techniques for dynamic
stochastic general equilibrium models. We cover the foundations of numerical …

Uncertainty is critical to questions about climate change policy. Recently developed
recursive integrated assessment models have become the primary tools for studying and …

We compute a sunspot equilibrium in an estimated small-scale New Keynesian model with a
zero lower bound (ZLB) constraint on nominal interest rates and a full set of stochastic …

We show how to enhance the performance of a Smolyak method for solving dynamic
economic models. First, we propose a more efficient implementation of the Smolyak method …

Should the generosity of unemployment benefits and the progressivity of income taxes
depend on the presence of business cycles? This paper proposes a tractable model where …

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 flexible and scalable method for computing global solutions of high‐
dimensional stochastic dynamic models. Within a time iteration or value function iteration …

Computational methods are required to solve problems without closed-form solutions in
environmental and resource economics. Efficiency, stability, and accuracy are key elements …