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Statistical inference in micro-simulation models: incorporating external information

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  • Anders Klevmarken, N.

Abstract

In practical applications of micro-simulation models (MSMs), 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 MSMs will loose in credibility. Differences between inference in static and dynamic models are noted and then the paper focuses on the estimation of behavioral parameters. There are four themes: calibration viewed as estimation subject to external constraints, piece meal versus system-wide estimation, simulation-based estimation and validation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:matcom:v:59:y:2002:i:1:p:255-265
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    1. Merz, Joachim, 1994. "Microdata Adjustment by the Minimum Information Loss Principle," MPRA Paper 7231, University Library of Munich, Germany.
    2. N. Anders Klevmarken, 1997. "Behavioral Modeling in Micro Simulation Models. A Survey," Working Paper Series 1997:31, Uppsala University, Department of Economics.
    3. 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.
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    Cited by:

    1. 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.
    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. 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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.

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    More about this item

    Keywords

    Micro-simulation; Alignment; Constrained estimation; Simulation estimation; Validation;
    All these keywords.

    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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