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...

We introduce a novel approach to solving dynamic programming problems, such as those in many economic models, on a quantum annealer, a specialized device that performs combinatorial optimization. Quantum annealers attempt to solve an NP-hard problem by starting in a quantum superposition of all...

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...

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...

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...

This article deals with the integration of taxes into real option-based investment models under risk neutrality and risk averison. It compares the possible approaches dynamic programming and contingent claims analysis to analyze their effects on the optimal investment rules before and after...