Spooky Boundaries at a Distance: Inductive Bias and Dynamic Macroeconomic Models

Abstract

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. This characteristic enables ML algorithms to solve for transition dynamics, ensuring that long-run boundary conditions are approximately met. ML can even select the correct path in cases of steady-state multiplicity.