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Exploration of the Parameter Space in Macroeconomic Models

Author

Listed:
  • Karl Naumann-Woleske

    (LadHyX - Laboratoire d'hydrodynamique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique)

  • Max Sina Knicker

    (TUM - Technische Universität Munchen - Technical University Munich - Université Technique de Munich)

  • Michael Benzaquen

    (LadHyX - Laboratoire d'hydrodynamique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique)

  • Jean-Philippe Bouchaud

    (Académie des sciences [Paris, France])

Abstract

Agent-Based Models (ABM) are computational scenario-generators, which can be used to predict the possible future outcomes of the complex system they represent. To better understand the robustness of these predictions, it is necessary to understand the full scope of the possible phenomena the model can generate. Most often, due to high-dimensional parameter spaces, this is a computationally expensive task. Inspired by ideas coming from systems biology, we show that for multiple macroeconomic models, including an agent-based model and several Dynamic Stochastic General Equilibrium (DSGE) models, there are only a few stiff parameter combinations that have strong effects, while the other sloppy directions are irrelevant. This suggests an algorithm that efficiently explores the space of parameters by primarily moving along the stiff directions. We apply our algorithm to a medium-sized agent-based model, and show that it recovers all possible dynamics of the unemployment rate. The application of this method to Agent-based Models may lead to a more thorough and robust understanding of their features, and provide enhanced parameter sensitivity analyses. Several promising paths for future research are discussed.

Suggested Citation

  • Karl Naumann-Woleske & Max Sina Knicker & Michael Benzaquen & Jean-Philippe Bouchaud, 2022. "Exploration of the Parameter Space in Macroeconomic Models," Post-Print hal-03797418, HAL.
  • Handle: RePEc:hal:journl:hal-03797418
    Note: View the original document on HAL open archive server: https://hal.science/hal-03797418
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    References listed on IDEAS

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    1. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    2. Guus ten Broeke & George van Voorn & Arend Ligtenberg & Jaap Molenaar, 2021. "The Use of Surrogate Models to Analyse Agent-Based Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 24(2), pages 1-3.
    3. G. Dosi & M. C. Pereira & M. E. Virgillito, 2018. "On the robustness of the fat-tailed distribution of firm growth rates: a global sensitivity analysis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(1), pages 173-193, April.
    4. Giovanni Dosi & Andrea Roventini, 2019. "More is different ... and complex! the case for agent-based macroeconomics," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 1-37, March.
    5. Giorgio Fagiolo & Andrea Roventini, 2017. "Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-1.
    6. repec:hal:spmain:info:hdl:2441/13thfd12aa8rmplfudlgvgahff is not listed on IDEAS
    7. Arika Ligmann-Zielinska & Peer-Olaf Siebers & Nicholas R Magliocca & Dawn C. Parker & Volker Grimm & Jing Du & Martin Cenek & Viktoriia Radchuk & Nazia N. Arbab & Sheng Li & Uta Berger & Rajiv Paudel , 2020. "‘One Size Does Not Fit All’: A Roadmap of Purpose-Driven Mixed-Method Pathways for Sensitivity Analysis of Agent-Based Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(1), pages 1-6.
    8. Tesfatsion, Leigh S., 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," Staff General Research Papers Archive 5075, Iowa State University, Department of Economics.
    9. Leonardo Bargigli & Luca Riccetti & Alberto Russo & Mauro Gallegati, 2020. "Network calibration and metamodeling of a financial accelerator agent based model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(2), pages 413-440, April.
    10. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    11. Stanislao Gualdi & Marco Tarzia & Francesco Zamponi & Jean-Philippe Bouchaud, 2017. "Monetary policy and dark corners in a stylized agent-based model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(3), pages 507-537, October.
    12. Blake LeBaron & Leigh Tesfatsion, 2008. "Modeling Macroeconomies as Open-Ended Dynamic Systems of Interacting Agents," American Economic Review, American Economic Association, vol. 98(2), pages 246-250, May.
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