We present the first computational framework that can compute global solutions to very-high-dimensional dynamic stochastic economic models on arbitrary state space geometries.This framework can also resolve value and policy functions' local features and perform uncertainty quantification, in a...

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 adaptive sparse grids. We approximate the value function with a low number of points and recursively apply fast...

Carbon taxation has been studied primarily in social planner or infinitely lived agent models, which trade off the welfare of future and current generations. Such frameworks obscure the potential for carbon taxation to produce a generational win-win. This paper develops a large-scale, dynamic...

We propose a generic computational framework for solving large-scale infinite-horizon, discrete-time dynamic incentive problems with persistent hidden types. First, we combine set-valued dynamic programming techniques with unsupervised machine learning to determine irregularly-shaped feasible...

In this paper, we introduce the concept of "self-justified equilibria" as a tractable alternative to rational expectations equilibria in stochastic general equilibrium models with heterogeneous agents. A self-justified equilibrium is a temporary equilibrium where, in each period, agents trade in...

Carbon taxation is a widely proposed and in some countries already adopted means to limit anthropogenic climate change. This paper studies carbon taxation using an 18-region, 80- period overlapping generations model. We focus on carbon policy that delivers present and future mankind the highest...