Deep Learning for Solving Economic Models

Jesus Fernandez-Villaverde

No 34250, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: The ongoing revolution in deep learning is reshaping research across many fields, including economics. Its effects are especially clear in solving dynamic economic models. These models often lack closed-form solutions, so economists have long relied on numerical methods such as value function iteration, perturbation, and projection techniques. Unfortunately, these approaches suffer from the curse of dimensionality, which makes global solutions computationally infeasible as the number of state variables increases. Deep learning offers a different approach: flexible tools that solve dynamic economic models by minimizing residuals in equilibrium conditions, and that can handle high-dimensional problems. This development promises to broaden the scope of quantitative economics. I illustrate the approach using the neoclassical growth model.

JEL-codes: C45 C61 C63 C68 (search for similar items in EconPapers)
Date: 2025-09
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Working Paper: Deep Learning for Solving Economic Models (2025)
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