IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v30y2007i3p245-264.html
   My bibliography  Save this article

Validating and Calibrating Agent-Based Models: A Case Study

Author

Listed:
  • Carlo Bianchi
  • Pasquale Cirillo
  • Mauro Gallegati
  • Pietro Vagliasindi

Abstract

In this paper we deal with the validation of an agent-based model and, in particular, with the technical validation process, that is to say all the set of test and methods used to analyze if the results of a simulation agree with reality. Today, thanks to some important studies, validation techniques are more and more complete and reliable: many distributional and goodness-of-fit tests have been developed, while several graphical tools have been studied to give the researcher a quick comprehension of actual and simulated data. In particular, the aim of this paper is to propose a good way to calibrate and validate a simple agent-based model of industrial dynamics we have developed. To achieve our goal we consider actual micro-level data of a sample of Italian manufacturing firms included in the Centrale dei Bilanci's database for the period 1983-2001, with no missing data and reliable values. The sample has been selected on the basis of appropriate requisites we discuss further in this paper. The validation results (both graphical and analytical) are quite promising. As calibration process, we use the method of indirect inference due to Gourieroux and Monfort(1996) to guarantee more accurate parameters, minimizing the differences between simulated and actual data. Even in this case the results we get are promising
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Carlo Bianchi & Pasquale Cirillo & Mauro Gallegati & Pietro Vagliasindi, 2007. "Validating and Calibrating Agent-Based Models: A Case Study," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 245-264, October.
    • Handle: RePEc:kap:compec:v:30:y:2007:i:3:p:245-264
    DOI: 10.1007/s10614-007-9097-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10614-007-9097-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10614-007-9097-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Fujiwara, Yoshi, 2004. "Zipf law in firms bankruptcy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 337(1), pages 219-230.
    2. Ramsden, J.J. & Kiss-Haypál, Gy., 2000. "Company size distribution in different countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 277(1), pages 220-227.
    3. Mauro Gallegati & Gianfranco Giulioni & Nozomi Kichiji, 2003. "Complex Dynamics And Financial Fragility In An Agent-Based Model," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(03), pages 267-282.
    4. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    5. Giulio Bottazzi & Angelo Secchi, 2006. "Explaining the distribution of firm growth rates," RAND Journal of Economics, RAND Corporation, vol. 37(2), pages 235-256, June.
    6. Robert Axelrod, 1997. "Advancing the Art of Simulation in the Social Sciences," Working Papers 97-05-048, Santa Fe Institute.
    7. Manfred GILLI, & Peter WINKER, 2001. "Indirect Estimation of the Parameters of Agent Based Models of Financial Markets," FAME Research Paper Series rp38, International Center for Financial Asset Management and Engineering.
    8. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    9. Bruce C. Greenwald & Joseph E. Stiglitz, 1990. "Macroeconomic Models with Equity and Credit Rationing," NBER Chapters, in: Asymmetric Information, Corporate Finance, and Investment, pages 15-42, National Bureau of Economic Research, Inc.
    10. Gatti, Domenico Delli & Guilmi, Corrado Di & Gaffeo, Edoardo & Giulioni, Gianfranco & Gallegati, Mauro & Palestrini, Antonio, 2005. "A new approach to business fluctuations: heterogeneous interacting agents, scaling laws and financial fragility," Journal of Economic Behavior & Organization, Elsevier, vol. 56(4), pages 489-512, April.
    11. Paul Windrum & Giorgio Fagiolo & Alessio Moneta, 2007. "Empirical Validation of Agent-Based Models: Alternatives and Prospects," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-8.
    12. R. Quandt, 1966. "Old and new methods of estimation and the pareto distribution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 10(1), pages 55-82, December.
    13. Frank Hahn, 1985. "Money and Inflation," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262580624, December.
    14. Anders Klevmarken, 2022. "Statistical Inference in Micro-simulation Models: Incorporating External Information," International Journal of Microsimulation, International Microsimulation Association, vol. 15(1), pages 111-120.
    15. Gilli, M. & Winker, P., 2003. "A global optimization heuristic for estimating agent based models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 299-312, March.
    16. Bruce C. Greenwald & Joseph E. Stiglitz, 1993. "Financial Market Imperfections and Business Cycles," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(1), pages 77-114.
    17. repec:ebl:ecbull:v:15:y:2003:i:6:p:1-7 is not listed on IDEAS
    18. Bianchi, Carlo & Cirillo, Pasquale & Gallegati, Mauro & Vagliasindi, Pietro A., 2008. "Validation in agent-based models: An investigation on the CATS model," Journal of Economic Behavior & Organization, Elsevier, vol. 67(3-4), pages 947-964, September.
    19. Robert Axtell & Robert Axelrod & Joshua M. Epstein & Michael D. Cohen, 1995. "Aligning Simulation Models: A Case Study and Results," Working Papers 95-07-065, Santa Fe Institute.
    20. Delli Gatti, Domenico & Gallegati, Mauro & Giulioni, Gianfranco & Palestrini, Antonio, 2003. "Financial fragility, patterns of firms' entry and exit and aggregate dynamics," Journal of Economic Behavior & Organization, Elsevier, vol. 51(1), pages 79-97, May.
    21. Corrado Di Guilmi & Mauro Gallegati & Edoardo Gaffeo, 2003. "Power Law Scaling in the World Income Distribution," Economics Bulletin, AccessEcon, vol. 15(6), pages 1-7.
    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. Bianchi, Carlo & Cirillo, Pasquale & Gallegati, Mauro & Vagliasindi, Pietro A., 2008. "Validation in agent-based models: An investigation on the CATS model," Journal of Economic Behavior & Organization, Elsevier, vol. 67(3-4), pages 947-964, September.
    2. Pasquale Cirillo & Mauro Gallegati, 2012. "The Empirical Validation of an Agent-based Model," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 38(4), pages 525-547.
    3. Annalisa Fabretti, 2013. "On the problem of calibrating an agent based model for financial markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(2), pages 277-293, October.
    4. Assenza, T. & Delli Gatti, D. & Gallegati, M., 2007. "Heterogeneity and Aggregation in a Financial Accelerator Model," CeNDEF Working Papers 07-13, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    5. 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.
    6. 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.
    7. 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.
    8. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    9. Francesco Lamperti, 2015. "An Information Theoretic Criterion for Empirical Validation of Time Series Models," LEM Papers Series 2015/02, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    10. repec:hal:spmain:info:hdl:2441/5vt1fet9fq9o5pkgj2qh2vn1cm is not listed on IDEAS
    11. Edgardo Bucciarelli & Marcello Silvestri, 2013. "Hyman P. Minsky's unorthodox approach: recent advances in simulation techniques to develop his theoretical assumptions," Journal of Post Keynesian Economics, Taylor & Francis Journals, vol. 36(2), pages 299-324.
    12. Francesco Lamperti & Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Alessandro Sapio, 2018. "And then he wasn't a she : Climate change and green transitions in an agent-based integrated assessment model," Working Papers hal-03443464, HAL.
    13. Lamperti, F. & Dosi, G. & Napoletano, M. & Roventini, A. & Sapio, A., 2020. "Climate change and green transitions in an agent-based integrated assessment model," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    14. repec:hal:spmain:info:hdl:2441/20hflp7eqn97boh50no50tv67n is not listed on IDEAS
    15. Lamperti, F. & Dosi, G. & Napoletano, M. & Roventini, A. & Sapio, A., 2018. "Faraway, So Close: Coupled Climate and Economic Dynamics in an Agent-based Integrated Assessment Model," Ecological Economics, Elsevier, vol. 150(C), pages 315-339.
    16. Witte, Björn-Christopher, 2011. "Removing systematic patterns in returns in a financial market model by artificially intelligent traders," BERG Working Paper Series 82, Bamberg University, Bamberg Economic Research Group.
    17. 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.
    18. Leonardo Bargigli & Gabriele Tedeschi, 2013. "Major trends in agent-based economics," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(2), pages 211-217, October.
    19. repec:hal:spmain:info:hdl:2441/4hs7liq1f49gh9chdf7r17gam6 is not listed on IDEAS
    20. Gatti, Domenico Delli & Di Guilmi, Corrado & Gallegati, Mauro & Giulioni, Gianfranco, 2007. "Financial Fragility, Industrial Dynamics, And Business Fluctuations In An Agent-Based Model," Macroeconomic Dynamics, Cambridge University Press, vol. 11(S1), pages 62-79, November.
    21. Ashraf, Quamrul & Gershman, Boris & Howitt, Peter, 2017. "Banks, market organization, and macroeconomic performance: An agent-based computational analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 143-180.
    22. LeBaron Blake & Winker Peter, 2008. "Introduction to the Special Issue on Agent-Based Models for Economic Policy Advice," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 141-148, April.
    23. Tedeschi, Gabriele & Recchioni, Maria Cristina & Berardi, Simone, 2019. "An approach to identifying micro behavior: How banks’ strategies influence financial cycles," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 329-346.

    More about this item

    Keywords

    Validation; Calibration; Agent-based models; Indirect inference; Size distribution; Tail analysis; EVT;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

    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:kap:compec:v:30:y:2007:i:3:p:245-264. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.