Deep learning solutions of DSGE models: A technical report

Pierre Beck (), Pablo Garcia Sanchez, Alban Moura, Julien Pascal and Olivier Pierrard

No 184, BCL working papers from Central Bank of Luxembourg

Abstract: This technical report provides an introduction to solving economic models using deep learning techniques. We offer a simple yet rigorous overview of deep learning methods and their applicability to economic modeling. We illustrate these concepts using the benchmark of modern macroeconomic theory: the stochastic growth model. Our results emphasize how various choices related to the design of the deep learning solution affect the accuracy of the results, providing some guidance for potential users of the method. We also provide fully commented computer codes. Overall, our hope is that this report will serve as an accessible, useful entry point to applying deep learning techniques to solve economic models for graduate students and researchers interested in the field.

Keywords: Solutions of DSGE models; deep learning; artificial neural networks (search for similar items in EconPapers)
JEL-codes: C45 C60 C63 E13 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2024-05
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-dge
References: View references in EconPapers View complete reference list from CitEc
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Persistent link: https://EconPapers.repec.org/RePEc:bcl:bclwop:bclwp184

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