IDEAS home Printed from https://ideas.repec.org/p/bcl/bclwop/bclwp184.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://www.bcl.lu/en/publications/Working-papers/184/BCLWP184.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christiano, Lawrence J. & Fisher, Jonas D. M., 2000. "Algorithms for solving dynamic models with occasionally binding constraints," Journal of Economic Dynamics and Control, Elsevier, vol. 24(8), pages 1179-1232, July.
    2. Lepetyuk, Vadym & Maliar, Lilia & Maliar, Serguei, 2020. "When the U.S. catches a cold, Canada sneezes: A lower-bound tale told by deep learning," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
    3. Judd, Kenneth L. & Guu, Sy-Ming, 1997. "Asymptotic methods for aggregate growth models," Journal of Economic Dynamics and Control, Elsevier, vol. 21(6), pages 1025-1042, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aruoba, S. Boragan & Fernandez-Villaverde, Jesus & Rubio-Ramirez, Juan F., 2006. "Comparing solution methods for dynamic equilibrium economies," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2477-2508, December.
    2. Posch, Olaf & Trimborn, Timo, 2013. "Numerical solution of dynamic equilibrium models under Poisson uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2602-2622.
    3. José Cao-Alvira, 2010. "Finite Elements in the Presence of Occasionally Binding Constraints," Computational Economics, Springer;Society for Computational Economics, vol. 35(4), pages 355-370, April.
    4. Francisco (F.) Blasques & Marc Nientker, 2019. "Transformed Perturbation Solutions for Dynamic Stochastic General Equilibrium Models," Tinbergen Institute Discussion Papers 19-012/III, Tinbergen Institute, revised 09 Feb 2020.
    5. Tarik Ocaktan & Michel Juillard, 2008. "Méthodes de simulation des modèles stochastiques d'équilibre général," Économie et Prévision, Programme National Persée, vol. 183(2), pages 115-126.
    6. Posch, Olaf & Trimborn, Timo, 2010. "Numerical solution of continuous-time DSGE models under Poisson uncertainty," Hannover Economic Papers (HEP) dp-450, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    7. José Cao-Alvira, 2012. "Velocity Volatility Assessment of Monetary Shocks on Cash-in-Advance Economies," Computational Economics, Springer;Society for Computational Economics, vol. 40(3), pages 293-311, October.
    8. Christopher Heiberger & Alfred Maussner, 2018. "Business Cycle Uncertainty and Economic Welfare Revisited," Discussion Paper Series 335, Universitaet Augsburg, Institute for Economics.
    9. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    10. Dorofeenko, Victor & Lee, Gabriel S. & Salyer, Kevin D., 2010. "A new algorithm for solving dynamic stochastic macroeconomic models," Journal of Economic Dynamics and Control, Elsevier, vol. 34(3), pages 388-403, March.
    11. Lars Grüne & Willi Semmler, 2007. "Asset pricing with dynamic programming," Computational Economics, Springer;Society for Computational Economics, vol. 29(3), pages 233-265, May.
    12. Ambler, Steve & Pelgrin, Florian, 2010. "Time-consistent control in nonlinear models," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2215-2228, October.
    13. Manuel S. Santos, 2000. "Accuracy of Numerical Solutions using the Euler Equation Residuals," Econometrica, Econometric Society, vol. 68(6), pages 1377-1402, November.
    14. Heiberger, Christopher & Maußner, Alfred, 2020. "Perturbation solution and welfare costs of business cycles in DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    15. Richard S. J. Tol & In Chang Hwang & Frédéric Reynès, 2012. "The Effect of Learning on Climate Policy under Fat-tailed Uncertainty," Working Paper Series 5312, Department of Economics, University of Sussex Business School.
    16. Ruitu Xu & Yifei Min & Tianhao Wang & Zhaoran Wang & Michael I. Jordan & Zhuoran Yang, 2023. "Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning," Papers 2303.04833, arXiv.org.
    17. Richard Evans & Kerk Phillips, 2014. "OLG Life Cycle Model Transition Paths: Alternate Model Forecast Method," Computational Economics, Springer;Society for Computational Economics, vol. 43(1), pages 105-131, January.
    18. Guerra Vallejos, Ernesto & Bobenrieth Hochfarber, Eugenio & Bobenrieth Hochfarber, Juan & Wright, Brian D., 2021. "Solving dynamic stochastic models with multiple occasionally binding constraints," Economic Modelling, Elsevier, vol. 105(C).
    19. Shigeru Fujita, 2004. "Vacancy persistence," Working Papers 04-23, Federal Reserve Bank of Philadelphia.
    20. Kato, Ryo & Nishiyama, Shin-Ichi, 2005. "Optimal monetary policy when interest rates are bounded at zero," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 97-133, January.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bcl:bclwop:bclwp184. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bclgvlu.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.