Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/307378 
Year of Publication: 
2024
Series/Report no.: 
CESifo Working Paper No. 11448
Publisher: 
CESifo GmbH, Munich
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.
Subjects: 
deep learning
quantitative economics
JEL: 
C61
C63
E27
Document Type: 
Working Paper
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