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A Practical Guide to Inference in Simulation Models

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  • Thomas Brenner
  • Claudia Werker

Abstract

This paper introduces a categorization of simulation models. It provides an explicit overview of the steps that lead to a simulation model. We highlight the advantages and disadvantages of various simulation approaches by examining how they advocate different ways of constructing simulation models. To this end, it discusses a number of relevant methodological issues, such as how realistic simulation models are obtained and which kinds of inference can be used in a simulation approach. Finally, the paper presents a practical guide on how simulation should and can be conducted.

Suggested Citation

  • 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.
    • Handle: RePEc:esi:evopap:2006-02
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    References listed on IDEAS

    as
    1. Werker, C. & Brenner, T., 2004. "Empirical calibration of simulation models," Working Papers 04.13, Eindhoven Center for Innovation Studies.
    2. Fagiolo, Giorgio & Dosi, Giovanni, 2003. "Exploitation, exploration and innovation in a model of endogenous growth with locally interacting agents," Structural Change and Economic Dynamics, Elsevier, vol. 14(3), pages 237-273, September.
    3. repec:bla:econom:v:69:y:2002:i:274:p:229-43 is not listed on IDEAS
    4. 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.
    5. T. Brenner & P. Murmann, 2003. "The Use of Simulations in Developing," Papers on Economics and Evolution 2003-03, Philipps University Marburg, Department of Geography.
    6. Sidney Winter & Yuri Kaniovski & Giovanni Dosi, 2003. "A baseline model of industry evolution," Journal of Evolutionary Economics, Springer, vol. 13(4), pages 355-383, October.
    7. Eliasson, Gunnar & Johansson, Dan & Taymaz, Erol, 2004. "Simulating the New Economy," Structural Change and Economic Dynamics, Elsevier, vol. 15(3), pages 289-314, September.
    8. Tsionas, Efthymios G., 2000. "Bayesian model comparison by Markov chain simulation: Illustration using stock market data," Research in Economics, Elsevier, vol. 54(4), pages 403-416, December.
    9. John Creedy & Alan Duncan, 2002. "Behavioural Microsimulation with Labour Supply Responses," Journal of Economic Surveys, Wiley Blackwell, vol. 16(1), pages 1-39, February.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. John Foster & Jason Potts, 2009. "A micro-meso-macro perspective on the methodology of evolutionary economics: Integrating history, simulation and econometrics," Springer Books, in: Uwe Cantner & Jean-Luc Gaffard & Lionel Nesta (ed.), Schumpeterian Perspectives on Innovation, Competition and Growth, pages 53-68, Springer.
    2. João Ferreira Brito & Pedro Cosme Costa Vieira, 2013. "Economic Growth as the Result of Firms’ Aggregate Performance: Evidence from the OECD Countries," Economics and Management Research Projects: An International Journal, Open Access International Journals, vol. 3(1), pages 24-31, December.
    3. Sander Hoog, 2019. "Surrogate Modelling in (and of) Agent-Based Models: A Prospectus," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1245-1263, March.
    4. Robert Marks, 2007. "Validating Simulation Models: A General Framework and Four Applied Examples," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 265-290, October.
    5. Robert E. Marks, 2013. "Validation and Functional Complexity," Discussion Papers 2013-30, School of Economics, The University of New South Wales.
    6. Sander van der Hoog, 2017. "Deep Learning in (and of) Agent-Based Models: A Prospectus," Papers 1706.06302, arXiv.org.

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    More about this item

    Keywords

    Methodology; Simulation Models; Practical Guide;
    All these keywords.

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • B52 - Schools of Economic Thought and Methodology - - Current Heterodox Approaches - - - Historical; Institutional; Evolutionary; Modern Monetary Theory;
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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