Deep Learning for Solving Economic Models

Jesus Fernandez-Villaverde
Additional contact information
Jesus Fernandez-Villaverde: University of Pennsylvania and NBER

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: The ongoing revolution in artificial intelligence, especially deep learning, is transforming research across many fields, including economics. Its impact is particularly strong in solving equilibrium economic models. These models often lack closed-form solutions, so economists have relied on numerical methods such as value function iteration, perturbation, and projection techniques. While powerful, these approaches face the curse of dimensionality, making global solutions computationally infeasible as the number of state variables increases. Recent advances in deep learning offer a new paradigm: flexible tools that efficiently approximate complex functions, manage high-dimensional problems, and expand the reach of quantitative economics. After introducing the basic concepts of deep learning, I illustrate the approach with the neoclassical growth model and discuss related ideas, including the double descent phenomenon and implicit regularization.

Keywords: Deep learning; equilibrium models; solution methods (search for similar items in EconPapers)
JEL-codes: C45 C61 C63 C68 (search for similar items in EconPapers)
Pages: 57 pages
Date: 2025-09-08
References: Add references at CitEc
Citations:

Downloads: (external link)
https://economics.sas.upenn.edu/system/files/worki ... per%20Submission.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 403 Forbidden

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:25-017

Access Statistics for this paper

More papers in PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania 133 South 36th Street, Philadelphia, PA 19104. Contact information at EDIRC.
Bibliographic data for series maintained by Administrator ().