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Network Calibration and Metamodeling of a Financial Accelerator Agent Based Model


  • Leonardo Bargigli

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

  • Luca Riccetti
  • Alberto Russo
  • Mauro Gallegati


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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    Cited by:

    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. 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.
    3. repec:spr:jeicoo:v:13:y:2018:i:1:d:10.1007_s11403-017-0193-4 is not listed on IDEAS
    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. Sylvain Barde & Sander van der Hoog, 2017. "An empirical validation protocol for large-scale agent-based models," Studies in Economics 1712, School of Economics, University of Kent.
    6. 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.
    7. Sylvain Barde & Sander Van Der Hoog, 2017. "An empirical validation protocol for large-scale agent-based models," Sciences Po publications 17/12, Sciences Po.
    8. repec:zbw:ifweej:201862 is not listed on IDEAS
    9. Chen, Siyan & Desiderio, Saul, 2018. "Computational evidence on the distributive properties of monetary policy," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 12, pages 1-32.
    10. 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.

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