Taming the Curse of Dimensionality:Quantitative Economics with Deep Learning

Jesus Fernandez-Villaverde, Galo Nuño Barrau and Jesse Perla
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Jesse Perla: University of British Columbia

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

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.

Keywords: Deep learning; quantitative economics (search for similar items in EconPapers)
JEL-codes: C61 C63 E27 (search for similar items in EconPapers)
Pages: 50 pages
Date: 2024-10-29
New Economics Papers: this item is included in nep-big, nep-cmp and nep-dge
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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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