We propose a new method for solving high-dimensional dynamic programming problems and recursive competitive equilibria with a large (but finite) number of heterogeneous agents using deep learning. The "curse of dimensionality" is avoided due to four complementary techniques: (1) exploiting...

This paper develops a dynamic programming method when the one-stage deviation principle in the sense of mechanism design literature doesn’t hold. The commonly used dynamic programming method is valid only if the one-stage deviation principle in the sense of mechanism design literature is...

Anthropogenic greenhouse gas emissions are changing the energy balance of our planet. Various climatic feedbacks make the resulting warming over the next decades and centuries highly uncertain. We quantify how this uncertainty changes the optimal carbon tax in a stochastic dynamic programming...

Previous research on optimal R&D subsidies has focussed on the long run. This paper characterizes the optimal time path of R&D subsidization in a semi-endogenous growth model, by exploiting a recently developed numerical method. Starting from the steady state under current R&D subsidization in...

Climate modelers have recognized the possibility of abrupt climate changes caused by a reorganization of the North Atlantic's current pattern (technically known as a thermohaline circulation collapse). This circulation system now warms north-western Europe and transports carbon dioxide to the...

We introduce a "smart" cap and trade system that eliminates the welfare costs of asymmetric information (“uncertainty”). This cap responds endogenously to technology or macroeconomic shocks, relying on the market price of certificates to aggregate information. It allows policy makers to...

In this paper, we apply a convex hull approach to counterfactual analysis of trade openness and growth. The experiments we choose evaluate the importance of trade openness for growth across African countries. Specifically, we ask the question what would happen if African countries were more...