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Validating and Calibrating Agent-Based Models: A Case Study

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Abstract

In this paper we deal with some validation and calibration experiments on a modified version of the Complex Adaptive Trivial System (CATS) model proposed in Gallegati et al. (2005 Journal of Economic Behavior and Organization, 56, 489–512). The CATS model has been extensively used to replicate a large number of scaling types stylized facts with a remarkable degree of precision. For such purposes, the simulation of the model has been performed entering ad hoc parameter values and using the same initial set up for all the agents involved in the experiments. Nowadays alternative robust and reliable validation techniques for determining whether the simulation model is an acceptable representation of the real system are available. Moreover many distributional and goodness-of-fit tests have been developed while several graphical tools have been proposed to give the researcher a quick comprehension of actual and simulated data. This paper discusses some validation experiments performed with the modified CATS model. In particular starting from a sample of Italian firms included in the CEBI database, we perform several ex-post validation experiments over the simulation period 1982–2000. In the experiments, the model parameters have been estimated using actual data and the initial set up consists of a sample of agents in 1982. The CATS model is then simulated over the period 1982–2000. Using alternative validation techniques, the simulations’ results are ex-post validated with respect to the actual data. The results are promising in that they show the good capabilities of the CATS model in reproducing the observed reality. Finally we have performed a first calibration experiment via indirect inference, in order to ameliorate our estimates. Even in this case, the results are interesting.

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References

  • Axelrod R. (1997). Advancing the art of simulation in the social sciences. In: Conte R., Hegselmann R., Terna P. (eds) Simulating social phenomena. Berlin, Springer-Verlag, pp. 21–40

    Google Scholar 

  • Axtell R. (2000). Why agents? On the varied motivations for agent computing in the social sciences. Center on Social and Economic Dynamics, Working Paper 17.

  • Axtell R. (2001). Zipf’s distribution of US firms sizes. Sciences 293: 1818–1820

    Google Scholar 

  • Axtell R., Axelrdod R., Epstein J.M., Cohen M.D. (1996). Aligning simulation models: A case study and results. Computational and Mathematical Organization Theory 1, 123–141

    Article  Google Scholar 

  • Bianchi C., Cirillo P., Gallegati M., Vagliasindi P. (2007). Validation in agent-based models: An investigation on the CATS model. Journal of Economic Behaviour and Organization, forthcoming.

  • Bottazzi G., Secchi A. (2005). Explaining the distribution of firm growth rates. Rand Journal of Economics 37, 234–263

    Google Scholar 

  • Carley, K. (1996). Validating computational models. Working Paper: www.econ.iastate.edu/tesfatsi/EmpVal/EmpVal.Carley.pdf

  • Cirillo, P. (2007). Some considerations about Gibrat’s law in Italy. Economics Letters, forthcoming.

  • Embrechts P., Mikosch T., Kluppelberg C. (1997). Modelling extremal events. Berlin and New York, Springer-Verlag

    Google Scholar 

  • Epstein J. (1999). Agent-based computational models and generative social sciences. Complexity 4, 41–60

    Article  Google Scholar 

  • Fagiolo G., Moneta A., Windrum P. (2007). Empirical validation of agent-based models: Alternatives and Prospects. Journal of Artificial Societies and Social Simulation 10(2): 8

    Google Scholar 

  • Fujiwara Y. (2004). Zipf law in firms bankruptcy. Physica A 337, 219–230

    Article  Google Scholar 

  • Gabaix X., Gopikrishnan P., Plerou V., Stanley H.E. (2003). A theory of power law distributions in financial markets fluctuations. Nature 423, 267–270

    Article  Google Scholar 

  • Gaffeo E., Di Guilmi C., Gallegati M. (2003). Power law scaling in the world income distribution. Economics Bullettin 15, 1–7

    Google Scholar 

  • Gallegati M., Giulioni G., Palestrini A., Delli Gatti D. (2003a). Financial fragility, patterns of firms’ entry and exit and aggregate dynamics. Journal of Economic Behavior and Organization 51, 79–97

    Article  Google Scholar 

  • Gallegati M., Giulioni G., Kichiji N. (2003b). Complex dynamics and financial fragility in an agent-based model. Advances in Complex Systems 6, 770–779

    Article  Google Scholar 

  • Gallegati M., Delli Gatti D., Di Guilmi C., Gaffeo E., Giulioni G., Palestrini A. (2004). Business cycles fluctuations and firms’ size distribution dynamics. Advances in Complex Systems 7, 1–18

    Article  Google Scholar 

  • Gallegati M., Delli Gatti D., Di Guilmi C., Gaffeo E., Giulioni G., Palestrini A. (2005). A new approach to business fluctuations: Heterogeneous interacting agents, scaling laws and financial fragility. Journal of Economic Behavior and Organization 56, 489–512

    Article  Google Scholar 

  • Gallegati M., Delli Gatti D., Gaffeo E., Giulioni G., Kirman A., Palestrini A., Russo A. (2007). Complex dynamics and empirical evidence. Information Science 177: 1202–1221

    Google Scholar 

  • Gilli M., Winker P. (2003). A global optimization heuristic for estimating agent-based models. Computational Statistics and Data Analysis 42, 299–312

    Article  Google Scholar 

  • Gourieroux C., Monfort A. (1996). Simulation-based econometric methods. Oxford, Oxford University Press

    Google Scholar 

  • Greenwald B.C., Stiglitz J.E. (1990). Macroeconomic models with equity and credit rationing. In: Hubbard R. (eds) Information, capital markets and investment. Chicago, Chicago University Press

    Google Scholar 

  • Greenwald B.C., Stiglitz J.E. (1993). Financial market imperfections and business cycles. The Quarterly Journal of Economics 108, 77–114

    Article  Google Scholar 

  • Hahn F. (1982). Money and inflation. Oxford, Blackwell Publishing

    Google Scholar 

  • Hall B.E. (1987). The relationship between firm size and growth. Journal of Industrial Economics 35, 583–606

    Article  Google Scholar 

  • Ijiri Y., Simon H.A. (1977). Skew distributions and the size of business firms. Amsterdam, North Holland

    Google Scholar 

  • Kaldor N. (1965). Capital accumulation and economic growth. In: Lutz F.A., Hague D.C. (eds) The theory of capital Proceedings of a Conference held by the International Economic Association. London, MacMillan

    Google Scholar 

  • Kleiber C., Kotz S. (2003). Statistical size distributions in economics and actuarial sciences. New York, Wiley

    Google Scholar 

  • Kleijnen J.P.C. (1998). Experimental design for sensitivity analysis, optimization and validation of simulation models. In: Banks J. (eds) Handbook of simulation (Chap 6). New York, Wiley

    Google Scholar 

  • Klevmarken N.A. (1998). Statistical inference in microsimulation models: Incorporating external information. Working Paper of Uppsala University, Department of Economics.

  • Mandelbrot B. (1960). The Pareto-Lévy law and the distribution of income. International Economic Review 1, 79–106

    Article  Google Scholar 

  • Okuyama K., Takayasu H., Takayasu M. (1999). Zipf’s law in income distribution of companies. Physica A 269, 125–131

    Article  Google Scholar 

  • Prabhakar M.D.N., Xie M., Jiang R. (2003). Weibull models. New York, Wiley

    Google Scholar 

  • Quandt R.E. (1966a). On the size distribution of firms. American Economic Review 56, 416–432

    Google Scholar 

  • Quandt R.E. (1966b). Old and new methods of estimation and the pareto distribution. Metrika 10, 55–82

    Article  Google Scholar 

  • Ramsden J., Kiss-Haypal G. (2000). Company size distribution in different countries. Physica A 277, 220–227

    Article  Google Scholar 

  • Sargent T.J. (1998). Verification and validation in simulation models. Proceedings of 1998 Winter Simulation Conference, pp. 52–64.

  • Shao J. (2003). Mathematical statistics. New York, Springer-Verlag

    Google Scholar 

  • Simon H.A. (1955). On a class of skew distribution functions. Biometrika 42, 425–440

    Google Scholar 

  • Stanley M., Amaral L., Buldyrev S., Havling S., Leshorn H., Maas P., Salinger M., Stanley E. (1996). Scaling behavior in the growth of companies. Nature 379, 804–806

    Article  Google Scholar 

  • Subbotin M.T. (1923). The law of frequency of error. Mathematicheskii Sbornik 31, 296–301

    Google Scholar 

  • Tesfatsion, L. (2007). Website on Validation of ACE: http://www.econ.iastate.edu/tesfatsi/empvalid.htm

  • Tesfatsion L., Judd K. (2006). Handbook of computational economics 2. Amsterdam: North Holland.

  • Troitzsch, K. (2004). Validating simulation models. Proceedings of the 18th European Simulation Multiconference, pp. 98–106.

  • Vagliasindi P., Cirillo P., Verga G. (2006). Imprese e mercato del credito in un modello agent-based. Rivista Internazionale di Scienze Sociali 114, 459–486

    Google Scholar 

  • Winker, P., & Gilli, M. (2001). Indirect estimation of parameters of agent based models of financial markets. Working Paper presented at the 2001 International Conference on Computing in Economics and Finance of the Society for Computational Economics.

  • Zipf G.K. (1932). Selective studies and the principle of relative frequency in language. Cambridge, Cambridge Press

    Google Scholar 

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Correspondence to Pasquale Cirillo.

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Bianchi, C., Cirillo, P., Gallegati, M. et al. Validating and Calibrating Agent-Based Models: A Case Study. Comput Econ 30, 245–264 (2007). https://doi.org/10.1007/s10614-007-9097-z

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