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Empirical Calibration of Simulation Models

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Author Info

  • 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

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 89.

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Date of creation: 11 Aug 2004
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Handle: RePEc:sce:scecf4:89

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Web page: http://comp-econ.org/
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Keywords: Simulations; Empirics; Methodology;

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References

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  1. Malerba, Franco, et al, 1999. "'History-Friendly' Models of Industry Evolution: The Computer Industry," Industrial and Corporate Change, Oxford University Press, vol. 8(1), pages 3-40, March.
  2. Johann Peter Murmann & Thomas Brenner, 2003. "The Use of Simulations in Developing Robust Knowledge about Causal Processes: Methodological Considerations and an Application to Industrial Evolution," Computing in Economics and Finance 2003 66, Society for Computational Economics.
  3. Finn E. Kydland & Edward C. Prescott, 1994. "The computational experiment: an econometric tool," Staff Report 178, Federal Reserve Bank of Minneapolis.
  4. 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.
  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. Franco Malerba & Luigi Orsenigo, 2002. "Innovation and market structure in the dynamics of the pharmaceutical industry and biotechnology: towards a history-friendly model," Industrial and Corporate Change, Oxford University Press, vol. 11(4), pages 667-703, August.
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Citations

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Cited by:
  1. Garavaglia, Christian, 2010. "Modelling industrial dynamics with "History-friendly" simulations," Structural Change and Economic Dynamics, Elsevier, vol. 21(4), pages 258-275, November.
  2. 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, vol. 9(1), pages 15.
  3. Thomas Brenner & Claudia Werker, 2007. "A Taxonomy of Inference in Simulation Models," Computational Economics, Society for Computational Economics, vol. 30(3), pages 227-244, October.
  4. John Foster, 2011. "Evolutionary macroeconomics: a research agenda," Journal of Evolutionary Economics, Springer, vol. 21(1), pages 5-28, February.
  5. 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.
  6. Garavaglia, C., 2004. "History friendly simulations for modelling industrial dynamics," Eindhoven Center for Innovation Studies (ECIS) working paper series 04.19, Eindhoven Center for Innovation Studies (ECIS).
  7. 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.
  8. Robin Cowan & Nicolas Jonard, 2007. "Structural holes, innovation and the distribution of ideas," Journal of Economic Interaction and Coordination, Springer, vol. 2(2), pages 93-110, December.
  9. 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 8.
  10. Thomas Brenner & Claudia Werker, 2006. "A Practical Guide to Inference in Simulation Models," Papers on Economics and Evolution 2006-02, Max Planck Institute of Economics, Evolutionary Economics Group.
  11. 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, Society for Computational Economics, vol. 30(3), pages 195-226, October.
  12. 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 10.
  13. 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.

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