IDEAS home Printed from https://ideas.repec.org/p/frz/wpaper/wp2016_01.rdf.html
   My bibliography  Save this paper

Network Calibration and Metamodeling of a Financial Accelerator Agent Based Model

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.disei.unifi.it/upload/sub/pubblicazioni/repec/pdf/wp01_2016.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Canova, Fabio & Sala, Luca, 2009. "Back to square one: Identification issues in DSGE models," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 431-449, May.
    2. Isabelle Salle & Murat Yıldızoğlu, 2014. "Efficient Sampling and Meta-Modeling for Computational Economic Models," Computational Economics, Springer;Society for Computational Economics, vol. 44(4), pages 507-536, December.
    3. Shin, Hyun Song, 2008. "Risk and liquidity in a system context," Journal of Financial Intermediation, Elsevier, vol. 17(3), pages 315-329, July.
    4. Roustant, Olivier & Ginsbourger, David & Deville, Yves, 2012. "DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i01).
    5. Riccetti, Luca & Russo, Alberto & Gallegati, Mauro, 2013. "Leveraged network-based financial accelerator," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1626-1640.
    6. Manzan, Sebastiano & Westerhoff, Frank H., 2007. "Heterogeneous expectations, exchange rate dynamics and predictability," Journal of Economic Behavior & Organization, Elsevier, vol. 64(1), pages 111-128, September.
    7. Thomas Brenner & Claudia Werker, 2007. "A Taxonomy of Inference in Simulation Models," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 227-244, October.
    8. Simone Alfarano & Thomas Lux & Friedrich Wagner, 2005. "Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 19-49, August.
    9. Bruce C. Greenwald & Joseph E. Stiglitz, 1993. "Financial Market Imperfections and Business Cycles," The Quarterly Journal of Economics, Oxford University Press, vol. 108(1), pages 77-114.
    10. Stoker, Thomas M, 1993. "Empirical Approaches to the Problem of Aggregation Over Individuals," Journal of Economic Literature, American Economic Association, vol. 31(4), pages 1827-1874, December.
    11. Alan P. Kirman, 1992. "Whom or What Does the Representative Individual Represent?," Journal of Economic Perspectives, American Economic Association, vol. 6(2), pages 117-136, Spring.
    12. Bargigli, Leonardo & Gallegati, Mauro, 2011. "Random digraphs with given expected degree sequences: A model for economic networks," Journal of Economic Behavior & Organization, Elsevier, vol. 78(3), pages 396-411, May.
    13. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," Review of Economic Studies, Oxford University Press, vol. 47(1), pages 225-238.
    14. Grazzini, Jakob & Richiardi, Matteo, 2015. "Estimation of ergodic agent-based models by simulated minimum distance," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 148-165.
    15. M. Gallegati & A. Palestrini & D. Gatti & E. Scalas, 2006. "Aggregation of Heterogeneous Interacting Agents: The Variant Representative Agent Framework," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 1(1), pages 5-19, May.
    16. Giorgio Fagiolo & Alessio Moneta & Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 195-226, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    4. Sylvain Barde & Sander Van Der Hoog, 2017. "An empirical validation protocol for large-scale agent-based models," Sciences Po publications 17/12, Sciences Po.
    5. 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.
    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. 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.
    8. 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.

    More about this item

    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:frz:wpaper:wp2016_01.rdf. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Giorgio Ricchiuti). General contact details of provider: http://edirc.repec.org/data/defirit.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.