We propose a ridgeless kernel method for solving infinite-horizon, deterministic, continuous-time models in economic dynamics, formulated as systems of differential-algebraic equations with asymptotic boundary conditions (e.g., transversality).
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), specifically deep learning, 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.