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Empirical Calibration of Simulation Models

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  • Claudia Werker
  • Thomas Brenner

Abstract

This paper discusses how the results of simulation models can be made more reliable and the method of simulating therefore more widely applicable. We suggested to calibrate simulation models empirically and developed a methodology based on Critical Realism in order to so. We suggested combining the procedures of two strands of literature: the empirical underpinning of the assumptions (like in microsimulations) and the empirical check of the implications (like in Bayesian inference). Both these strands of literature are mainly concerned with predicting future developments. We, instead, aim to infer statements about causal relations and characteristics of a set of systems or dynamics, such as, e.g., the development of an industry, that have a general validity for this set of systems or dynamics. In other words, instead of deriving probabilistic predictions of the future and statements of the current situation and dynamics of one single system we developed a methodology to gain general statements about the features of systems and dynamics.

Suggested Citation

  • Claudia Werker & Thomas Brenner, 2004. "Empirical Calibration of Simulation Models," Papers on Economics and Evolution 2004-10, Philipps University Marburg, Department of Geography.
  • Handle: RePEc:esi:evopap:2004-10
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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.
    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. T. Brenner & P. Murmann, 2003. "The Use of Simulations in Developing," Papers on Economics and Evolution 2003-03, Philipps University Marburg, Department of Geography.
    4. Paul Downward & John H. Finch & John Ramsay, 2002. "Critical realism, empirical methods and inference: a critical discussion," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 26(4), pages 481-500, July.
    5. Machlup, Fritz, 1978. "Methodology of Economics and Other Social Sciences," Elsevier Monographs, Elsevier, edition 1, number 9780124645509 edited by Shell, Karl.
    6. Hariolf Grupp, 1998. "Foundations of the Economics of Innovation," Books, Edward Elgar Publishing, number 1390.
    7. 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 and the Associazione ICC, vol. 11(4), pages 667-703, August.
    8. 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.
    9. 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.
    10. Malerba, Franco, et al, 1999. "'History-Friendly' Models of Industry Evolution: The Computer Industry," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 8(1), pages 3-40, March.
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    More about this item

    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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