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Network Calibration and Metamodeling of a Financial Accelerator Agent Based Model

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  • Leonardo Bargigli

    (Dipartimento di Scienze per l'Economia e l'Impresa)

  • Luca Riccetti
  • Alberto Russo
  • Mauro Gallegati

Abstract

We allow firms and banks to entertain multiple credit connections in a financially constrained production framework, resorting to a random network model whose parameters are calibrated with real data. The calibration is successful since the network model is able to reproduce the degree and strength (debt and loan) distributions of the Japanese credit market. We run simulations over the parameter space using an efficient design, and compare a number of alternative statistical metamodels in order to select the best specification for the relationship between the parameters and a set of endogenous variables of the model. We show that the metamodeling approach can be usefully extended to economic models in order to bridge the gap between micro and macro variables through a rigorous statistical analysis of ABMs, without imposing unrealistic restrictions on the micro model such as the representative agent hypothesis.

Suggested Citation

  • Leonardo Bargigli & Luca Riccetti & Alberto Russo & Mauro Gallegati, 2016. "Network Calibration and Metamodeling of a Financial Accelerator Agent Based Model," Working Papers - Economics wp2016_01.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
  • Handle: RePEc:frz:wpaper:wp2016_01.rdf
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    References listed on IDEAS

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    Cited by:

    1. Luca Riccetti & Alberto Russo & Mauro Gallegati, 2022. "Firm–bank credit network, business cycle and macroprudential policy," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(2), pages 475-499, April.
    2. Karl Naumann-Woleske & Max Sina Knicker & Michael Benzaquen & Jean-Philippe Bouchaud, 2021. "Exploration of the Parameter Space in Macroeconomic Agent-Based Models," Papers 2111.08654, arXiv.org, revised Aug 2022.
    3. 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.
    4. 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.
    5. Mitja Steinbacher & Matthias Raddant & Fariba Karimi & Eva Camacho Cuena & Simone Alfarano & Giulia Iori & Thomas Lux, 2021. "Advances in the agent-based modeling of economic and social behavior," SN Business & Economics, Springer, vol. 1(7), pages 1-24, July.
    6. Sylvain Barde & Sander van der Hoog, 2017. "An empirical validation protocol for large-scale agent-based models," Working Papers hal-03458672, HAL.
    7. Catullo, Ermanno & Gallegati, Mauro & Russo, Alberto, 2022. "Forecasting in a complex environment: Machine learning sales expectations in a stock flow consistent agent-based simulation model," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    8. 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.
    9. Siyan Chen & Saul Desiderio, 2022. "A Regression-Based Calibration Method for Agent-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 687-700, February.
    10. 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.
    11. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    12. Chen, Siyan & Desiderio, Saul, 2018. "Computational evidence on the distributive properties of monetary policy," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 12, pages 1-32.
    13. Elizabeth Jane Casabianca & Alessia Lo Turco & Daniela Maggioni, 2021. "Migration And The Structure Of Manufacturing Production. A View From Italian Provinces," Working Papers 448, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    14. Ciola, Emanuele & Gaffeo, Edoardo & Gallegati, Mauro, 2022. "Search for profits and business fluctuations: How does banks’ behaviour explain cycles?," Journal of Economic Dynamics and Control, Elsevier, vol. 135(C).
    15. Brancaccio, Emiliano & Giammetti, Raffaele & Lopreite, Milena & Puliga, Michelangelo, 2018. "Centralization of capital and financial crisis: A global network analysis of corporate control," Structural Change and Economic Dynamics, Elsevier, vol. 45(C), pages 94-104.
    16. Hung-Wen Lin & Jing-Bo Huang & Kun-Ben Lin & Shu-Heng Chen, 2022. "The competitions of time-varying and constant loadings in asset pricing models: empirical evidence and agent-based simulations," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(2), pages 577-612, April.
    17. Emanuele Ciola & Edoardo Gaffeo & Mauro Gallegati, 2021. "Search for Profits and Business Fluctuations: How Banks' Behaviour Explain Cycles?," Working Papers 450, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

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