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Taming the Curse of Dimensionality:Quantitative Economics with Deep Learning

Author

Listed:
  • Jesús Fernández-Villaverde

    (University of Pennsylvania, CEPR and NBER)

  • Galo Nuno

    (Banco de Espana, CEPR, CEMFI)

  • Jesse Perla

    (University of British Columbia)

Abstract
We argue that deep learning provides a promising avenue for taming the curse of dimensionality in quantitative economics. We begin by exploring the unique challenges posed by solving dynamic equilibrium models, especially the feedback loop between individual agents’ decisions and the aggregate consistency conditions required by equilibrium. Following this, we introduce deep neural networks and demonstrate their application by solving the stochastic neoclassical growth model. Next, we compare deep neural networks with traditional solution methods in quantitative economics. We conclude with a survey of neural network applications in quantitative economics and offer reasons for cautious optimism.

Suggested Citation

  • Jesús Fernández-Villaverde & Galo Nuno & Jesse Perla, 2024. "Taming the Curse of Dimensionality:Quantitative Economics with Deep Learning," PIER Working Paper Archive 24-034, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:24-034
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    Keywords

    Deep learning; quantitative economics;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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