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We argue that deep learning provides a promising avenue for taming the curse of dimensionality in quantitative economics.

This paper shows how kernel based methods can automatically fulfill long-run boundary conditions for dynamic economic models, and provide an alternative to classical methods - even in low dimensions.

In the long run, we are all dead. Nonetheless, when studying the short-run dynamics of economic models, it is crucial to consider boundary conditions that govern long-run, forward-looking behavior, such as transversality conditions. We demonstrate that machine learning (ML) can automatically satisfy these conditions due to its inherent inductive bias toward finding flat solutions to functional equations.

We provide a new method for solving high-dimensional dynamic programming problems, and recursive competitive equilibria with a large (but finite) number of heterogenous agents.