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Deep learning solutions of DSGE models: A technical report

Author

Listed:
  • Pierre Beck
  • Pablo Garcia Sanchez
  • Alban Moura
  • Julien Pascal
  • Olivier Pierrard

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.

Suggested Citation

  • Pierre Beck & Pablo Garcia Sanchez & Alban Moura & Julien Pascal & Olivier Pierrard, 2024. "Deep learning solutions of DSGE models: A technical report," BCL working papers 184, Central Bank of Luxembourg.
    • Handle: RePEc:bcl:bclwop:bclwp184
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    File URL: https://www.bcl.lu/en/publications/Working-papers/184/BCLWP184.pdf
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    More about this item

    Keywords

    Solutions of DSGE models; deep learning; artificial neural networks;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical

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