IDEAS home Printed from https://ideas.repec.org/p/pen/papers/25-017.html
   My bibliography  Save this paper

Deep Learning for Solving Economic Models

Author

Listed:
  • Jesus Fernandez-Villaverde

    (University of Pennsylvania and NBER)

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.

Suggested Citation

  • Jesus Fernandez-Villaverde, 2025. "Deep Learning for Solving Economic Models," PIER Working Paper Archive 25-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:25-017
    as

    Download full text from publisher

    File URL: https://economics.sas.upenn.edu/system/files/working-papers/25-017%20PIER%20Paper%20Submission.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pen:papers:25-017. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Administrator (email available below). General contact details of provider: https://edirc.repec.org/data/deupaus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.