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Statistical Inference in Micro Simulation Models: Incorporating external information

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  • Klevmarken, N. Anders

    () (Department of Economics)

Abstract

In practical applications of micro simulation models very little is usually known about the properties of the simulated values. This paper argues that we need to apply the same rigorous standards for inference in micro simulation work as in scientific work generally. If not, then micro simulation models will loose in credibility. The paper first discusses how the structure of the model will determine inference and then follow sections on estimation and validation. Differences between inference in static and dynamic models are noted and then the paper focuses on the estimation of behavioral parameters. There are three themes: calibration viewed as estimation subject to external constraints, piece meal vs. system-wide estimation, and simulation based estimation.

Suggested Citation

  • Klevmarken, N. Anders, 1998. "Statistical Inference in Micro Simulation Models: Incorporating external information," Working Paper Series 1998:20, Uppsala University, Department of Economics.
  • Handle: RePEc:hhs:uunewp:1998_020
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    1. Anders Klevmarken, N., 2002. "Statistical inference in micro-simulation models: incorporating external information," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 59(1), pages 255-265.
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    Cited by:

    1. Matteo Richiardi & Ross E. Richardson, 2017. "JAS-mine: A new platform for microsimulation and agent-based modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 106-134.
    2. Gaël Dupont & Cyrille Hagneré & Vincent Touzé, 2003. "Les modèles de microsimulation dynamique dans l'analyse des réformes des systèmes de retraites : une tentative de bilan," Economie & Prévision, La Documentation Française, vol. 0(4), pages 167-191.
    3. Michal Myck & Mateusz Najsztub, 2015. "Data and Model Cross-validation to Improve Accuracy of Microsimulation Results: Estimates for the Polish Household Budget Survey," International Journal of Microsimulation, International Microsimulation Association, vol. 8(1), pages 33-66.
    4. Eugenio Zucchelli & Andrew M Jones & Nigel Rice, 2012. "The evaluation of health policies through dynamic microsimulation methods," International Journal of Microsimulation, International Microsimulation Association, vol. 5(1), pages 2-20.
    5. Jinjing Li & Cathal O'Donoghue, 2013. "A survey of dynamic microsimulation models: uses, model structure and methodology," International Journal of Microsimulation, International Microsimulation Association, vol. 6(2), pages 3-55.
    6. Anders Klevmarken, N., 2002. "Statistical inference in micro-simulation models: incorporating external information," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 59(1), pages 255-265.
    7. Zamac, Jovan & Hallberg, Daniel & Lindh, Thomas, 2008. "Low fertility and long run growth in an economy with a large public sector," Arbetsrapport 2008:11, Institute for Futures Studies.
    8. Zucchelli, E & Jones, A.M & Rice, N, 2010. "The evaluation of health policies through microsimulation methods," Health, Econometrics and Data Group (HEDG) Working Papers 10/03, HEDG, c/o Department of Economics, University of York.
    9. Pasquale Cirillo & Mauro Gallegati, 2012. "The Empirical Validation of an Agent-based Model," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 38(4), pages 525-547.
    10. Bianchi, Carlo & Cirillo, Pasquale & Gallegati, Mauro & Vagliasindi, Pietro A., 2008. "Validation in agent-based models: An investigation on the CATS model," Journal of Economic Behavior & Organization, Elsevier, vol. 67(3-4), pages 947-964, September.
    11. Carlo Bianchi & Pasquale Cirillo & Mauro Gallegati & Pietro Vagliasindi, 2007. "Validating and Calibrating Agent-Based Models: A Case Study," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 245-264, October.
    12. Tim Goedemé & Karel Van den Bosch & Lina Salanauskaite & Gerlinde Verbist, 2013. "Testing the Statistical Significance of Microsimulation Results: Often Easier than You Think. A Technical Note," ImPRovE Working Papers 13/10, Herman Deleeck Centre for Social Policy, University of Antwerp.

    More about this item

    Keywords

    Micro simulation; Alignment; Calibration; System-wide estimation; Simulation-based estimation;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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