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Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning

Author

Listed:
  • Fernández-Villaverde, Jesús
  • Nuño, Galo
  • Perla, Jesse

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

  • Fernández-Villaverde, Jesús & Nuño, Galo & Perla, Jesse, 2024. "Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning," CEPR Discussion Papers 19636, Centre for Economic Policy Research.
    • Handle: RePEc:cpr:ceprdp:19636
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    2. Ferrari Minesso, Massimo & Frenzel, Carla, 2026. "Sequential solution for DSGE models with deep neural networks," Working Paper Series 3236, European Central Bank.
    3. Hanno Kase & Matthias Rottner & Fabio Stohler, 2025. "Generative economic modeling," BIS Working Papers 1312, Bank for International Settlements.
    4. Andrzej Tokajuk & Jaros{l}aw A. Chudziak, 2025. "Partial multivariate transformer as a tool for cryptocurrencies time series prediction," Papers 2512.04099, arXiv.org.
    5. Yucheng Yang & Chiyuan Wang & Andreas Schaab & Benjamin Moll, 2025. "Structural Reinforcement Learning for Heterogeneous Agent Macroeconomics," Papers 2512.18892, arXiv.org.
    6. Federico Gabriele & Aldo Glielmo & Marco Taboga, 2025. "Heterogeneous RBCs via Deep Multi-Agent Reinforcement Learning," Papers 2510.12272, arXiv.org, revised Feb 2026.
    7. Hanno Kase & Leonardo Melosi & Sebastian Rast & Matthias Rottner, 2026. "The perils of narrowing fiscal spaces," BIS Working Papers 1328, Bank for International Settlements.
    8. Eftekhari, Aryan & Juillard, Michel & Rion, Normann & Scheidegger, Simon, 2026. "Scalable global solution techniques for high-dimensional models in Dynare," Journal of Economic Dynamics and Control, Elsevier, vol. 182(C).
    9. Li, Mingzhe, 2025. "A Theory of Portfolio Choice for Heterogeneous Investors," MPRA Paper 126642, University Library of Munich, Germany, revised 29 Oct 2025.

    More about this item

    Keywords

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    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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