Journal of Economic Literature
ISSN 0022-0515 (Print) | ISSN 2328-8175 (Online)
Deep Learning for Solving Economic Models
Journal of Economic Literature
(pp. 829–75)
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.Citation
Fernández- Villaverde, Jesús. 2026. "Deep Learning for Solving Economic Models." Journal of Economic Literature 64 (3): 829–75. DOI: 10.1257/jel.20261794Additional Materials
JEL Classification
- C45 Neural Networks and Related Topics
- C61 Optimization Techniques; Programming Models; Dynamic Analysis
- D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
- O41 One, Two, and Multisector Growth Models