Taming the curse of dimensionality: quantitative economics with deep learning

Jesús Fernández-Villaverde, Galo Nuño Barrau and Jesse Perla
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Jesús Fernández-Villaverde: UNIVERSITY OF PENNSYLVANIA, NBER, CEPR
Jesse Perla: UNIVERSITY OF BRITISH COLUMBIA

No 2444, Working Papers from Banco de España

Abstract: We argue that deep learning provides a promising approach to addressing the curse of dimensionality in quantitative economics. We begin by exploring the unique challenges involved in solving dynamic equilibrium models, particularly the feedback loop between individual agents’ decisions and the aggregate consistency conditions required to achieve equilibrium. We then 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 review of the applications of neural networks in quantitative economics and provide arguments for cautious optimism.

Keywords: deep learning; quantitative economics (search for similar items in EconPapers)
JEL-codes: C61 C63 E27 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2024-11
New Economics Papers: this item is included in nep-big, nep-dge and nep-gro
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Related works:
Working Paper: Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning (2024)
Working Paper: Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning (2024)
Working Paper: Taming the Curse of Dimensionality:Quantitative Economics with Deep Learning (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:bde:wpaper:2444

DOI: 10.53479/38233

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