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Empirical Calibration of Simulation Models

Author

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  • Thomas Brenner
  • Claudia Werker

Abstract

Simulations have become a common tool to study the implications of theoretical models. In addition, computational approaches are frequently used in statistics to adapt models to reality. We aim to merge these approaches. To obtain robust results with significant validity from simulations, empirical data have to be extensively used. We first discuss how inference in economic contexts can be made and then describe our proposed method. It makes extensive use of empirical knowledge for the development of a simulation model whose implications are then examined in the light of empirical data in a Bayesian-like approach. This allows all systems that can be described by the simulation model to be classified into subsets of models. This makes it possible to establish different subsets of models that describe certain realities and to study the characteristics of and causal relationships in these subsets

Suggested Citation

  • Thomas Brenner & Claudia Werker, 2004. "Empirical Calibration of Simulation Models," Computing in Economics and Finance 2004 89, Society for Computational Economics.
  • Handle: RePEc:sce:scecf4:89
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    References listed on IDEAS

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    1. Finn E. Kydland & Edward C. Prescott, 1996. "The Computational Experiment: An Econometric Tool," Journal of Economic Perspectives, American Economic Association, vol. 10(1), pages 69-85, Winter.
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    5. Schwerin, Joachim & Werker, Claudia, 2003. "Learning innovation policy based on historical experience," Structural Change and Economic Dynamics, Elsevier, vol. 14(4), pages 385-404, December.
    6. Dominique Foray & Robin Cowan, 2002. "Evolutionary economics and the counterfactual threat: on the nature and role of counterfactual history as an empirical tool in economics," Journal of Evolutionary Economics, Springer, vol. 12(5), pages 539-562.
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    Citations

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    Cited by:

    1. Robin Cowan & Nicolas Jonard, 2007. "Structural holes, innovation and the distribution of ideas," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 2(2), pages 93-110, December.
    2. Іllyusha S., 2015. "Modeling Ukraine's technological approaching to the developed countries," Economy and Forecasting, Valeriy Heyets, issue 3, pages 104-122.
    3. Tilmann Rave & Ursula Triebswetter, 2006. "Ökonomische Auswirkungen umweltpolitischer Regulierungen : eine Machbarkeitsstudie vor dem Hintergrund der Anforderungen der Richtlinie 96/61/EG über die integrierte Vermeidung und Verminderung von Um," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 30, October.
    4. Giorgio Fagiolo & Paul Windrum & Alessio Moneta, 2006. "Empirical Validation of Agent Based Models: A Critical Survey," LEM Papers Series 2006/14, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    5. Thomas Brenner & Claudia Werker, 2006. "A Practical Guide to Inference in Simulation Models," Papers on Economics and Evolution 2006-02, Philipps University Marburg, Department of Geography.
    6. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.
    7. Jonas Friege & Georg Holtz & Émile J.L. Chappin, 2016. "Exploring Homeowners’ Insulation Activity," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(1), pages 1-4.
    8. Garavaglia, Christian, 2010. "Modelling industrial dynamics with "History-friendly" simulations," Structural Change and Economic Dynamics, Elsevier, vol. 21(4), pages 258-275, November.
    9. Robinson, Scott A. & Rai, Varun, 2015. "Determinants of spatio-temporal patterns of energy technology adoption: An agent-based modeling approach," Applied Energy, Elsevier, pages 273-284.
    10. Stuart Rossiter & Jason Noble & Keith R.W. Bell, 2010. "Social Simulations: Improving Interdisciplinary Understanding of Scientific Positioning and Validity," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(1), pages 1-10.
    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. Piergiuseppe Morone & Richard Taylor, 2010. "Knowledge Diffusion and Innovation," Books, Edward Elgar Publishing, number 13143, April.
    13. 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.
    14. Matteo Richiardi & Roberto Leombruni & Nicole J. Saam & Michele Sonnessa, 2006. "A Common Protocol for Agent-Based Social Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, pages 1-15.
    15. 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.
    16. Marcelo De Oliveira Passos & Jean Rodrigues Venecian, 2016. "A Multi-Agent Computational Model For Brazilian Stock Market: The "Gap Value" Channel Of Monetary Policy Transmission Mechanism," Anais do XLII Encontro Nacional de Economia [Proceedings of the 42ndd Brazilian Economics Meeting] 044, ANPEC - Associação Nacional dos Centros de Pósgraduação em Economia [Brazilian Association of Graduate Programs in Economics].
    17. Garcia, Rosanna & Rummel, Paul & Hauser, John, 2007. "Validating agent-based marketing models through conjoint analysis," Journal of Business Research, Elsevier, vol. 60(8), pages 848-857, August.
    18. Lorenzo Zirulia, 2012. "Piergiuseppe Morone and Richard Taylor: Knowledge diffusion and innovation: modelling complex entrepreneurial behaviours," Journal of Evolutionary Economics, Springer, vol. 22(2), pages 395-400, April.
    19. Riccardo Boero & Flaminio Squazzoni, 2005. "Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-6.
    20. John Foster, 2015. "Joseph Schumpeter and Simon Kuznets: comparing their evolutionary economic approaches to business cycles and economic growth," Journal of Evolutionary Economics, Springer, vol. 25(1), pages 163-172, January.
    21. John Foster, 2011. "Evolutionary macroeconomics: a research agenda," Journal of Evolutionary Economics, Springer, vol. 21(1), pages 5-28, February.

    More about this item

    Keywords

    Simulations; Empirics; Methodology;

    JEL classification:

    • B40 - Schools of Economic Thought and Methodology - - Economic Methodology - - - General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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