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Statistical Inference in Micro Simulation Models: Incorporationg External Information

Author

Listed:
  • Klevmarken, N.A.

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 rigourous standards for inference in micro simulation work as in scientific worl generally. If not, then micro simulation models will loose in credibility.

Suggested Citation

  • Klevmarken, N.A., 1998. "Statistical Inference in Micro Simulation Models: Incorporationg External Information," Papers 1998:20, Uppsala - Working Paper Series.
    • Handle: RePEc:fth:uppaal:1998:20
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Anders Klevmarken, 2022. "Statistical Inference in Micro-simulation Models: Incorporating External Information," International Journal of Microsimulation, International Microsimulation Association, vol. 15(1), pages 111-120.
    5. Jovan Žamac & Daniel Hallberg & Thomas Lindh, 2010. "Low Fertility and Long-Run Growth in an Economy with a Large Public Sector [Fécondité basse et croissance à long terme dans une économie à secteur public très développé]," European Journal of Population, Springer;European Association for Population Studies, vol. 26(2), pages 183-205, May.
    6. 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.
    7. 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.
    8. 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.
    9. Tobias Schoch & André Müller, 2020. "Treatment of sample under-representation and skewed heavy-tailed distributions in survey-based microsimulation: An analysis of redistribution effects in compulsory health care insurance in Switzerland," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 14(3), pages 267-304, December.
    10. 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.
    11. Verbist, Gerlinde & Goedemé, Tim & Van den Bosch, Karel & Salanauskaite, Lina, 2013. "Testing the statistical significance of microsimulation results: often easier than you think. A technical note," EUROMOD Working Papers EM18/13, EUROMOD at the Institute for Social and Economic Research.
    12. 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.
    13. 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.
    14. John Creedy & Ivan Tuckwell, 2004. "Reweighting Household Surveys for Tax Microsimulation Modelling: An Application to the New Zealand Household Economic Survey," Australian Journal of Labour Economics (AJLE), Bankwest Curtin Economics Centre (BCEC), Curtin Business School, vol. 7(1), pages 71-88, March.
    15. Elisa Baroni & Matteo Richiardi, 2007. "Orcutt’s Vision, 50 years on," LABORatorio R. Revelli Working Papers Series 65, LABORatorio R. Revelli, Centre for Employment Studies.

    More about this item

    Keywords

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    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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