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The simulation properties of microsimulation models with static and dynamic ageing a brief guide into choosing one type of model over the other

Author

Listed:
  • Gijs Dekkers

    (Federal Planning Bureau and CESO KU Leuven and CEPS/INSTEAD)

Abstract
To assess possible distribution effects of alternative scenarios, including hypothetical future states, one can use either static ageing techniques, which age the population by reweighing and uprating, or dynamic ageing, which alter the relevant population by applying deterministic probabilities that a certain event may or may not occur. This paper makes the argument that, even though the two methods are technically completely different, they are not unlike in terms of their simulation properties. Starting from the thesis that under theoretical circumstances, both approaches are equivalent in terms of their simulation properties, the choice between the two archetypes of models comes down to assessing how far the actual and theoretical circumstances differ from each other. By highlighting the differences and resemblances between static and dynamic microsimulations in terms of their simulation properties, this short note will contribute to the debate in choosing between these two types of models, and can thus serve as an advice piece for someone contemplating the development of a microsimulation model

Suggested Citation

  • Gijs Dekkers, 2015. "The simulation properties of microsimulation models with static and dynamic ageing a brief guide into choosing one type of model over the other," International Journal of Microsimulation, International Microsimulation Association, vol. 8(1), pages 97-109.
    • Handle: RePEc:ijm:journl:v:8:y:2015:i:1:p:97-109
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    File URL: http://www.microsimulation.org/IJM/V8_1/5_IJM_8_1_Dekkers.pdf
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    References listed on IDEAS

    as
    1. Judith Flory & Sven Stöwhase, 2012. "MIKMOD-ESt: A Static Microsimulation Model of Personal Income Taxation in Germany," International Journal of Microsimulation, International Microsimulation Association, vol. 5(2), pages 66-73.
    2. Merz, Joachim, 1994. "Microdata Adjustment by the Minimum Information Loss Principle," MPRA Paper 7231, University Library of Munich, Germany.
    3. Daniele Pacifico, 2011. "SREWEIGHT: Stata module for survey reweighting," Statistical Software Components S457312, Boston College Department of Economics, revised 23 Jan 2014.
    4. Azizur Rahman & Ann Harding & Robert Tanton & Shuangzhe Liu, 2010. "Methodological Issues in Spatial Microsimulation Modelling for Small Area Estimation," International Journal of Microsimulation, International Microsimulation Association, vol. 3(2), pages 3-22.
    5. John Creedy & Guyonne Kalb, 2006. "Labour Supply and Microsimulation," Books, Edward Elgar Publishing, number 4236.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. GENEVOIS Anne-Sophie & LIEGEOIS Philippe & PI ALPERIN Maria Noel, 2019. "DyMH_LU: a simple tool for modelling and simulating the health status of the Luxembourgish elderly in the longer run," LISER Working Paper Series 2019-06, Luxembourg Institute of Socio-Economic Research (LISER).
    2. Cathal O'Donoghue & Gijs Dekkers, 2018. "Increasing the Impact of Dynamic Microsimulation Modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 61-96.
    3. Li, Jinjing & O'Donoghue, Cathal, 2012. "A methodological survey of dynamic microsimulation models," MERIT Working Papers 2012-002, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    4. Burgard Jan Pablo & Dieckmann Hanna & Krause Joscha & Merkle Hariolf & Münnich Ralf & Neufang Kristina M. & Schmaus Simon, 2020. "A generic business process model for conducting microsimulation studies," Statistics in Transition New Series, Statistics Poland, vol. 21(4), pages 191-211, August.
    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. Maria Ana Matias & Rita Santos & Panos Kasteridis & Katja Grasic & Anne Mason & Nigel Rice, 2022. "Approaches to projecting future healthcare demand," Working Papers 186cherp, Centre for Health Economics, University of York.
    7. Jan Pablo Burgard & Hanna Dieckmann & Joscha Krause & Hariolf Merkle & Ralf Münnich & Kristina M. Neufang & Simon Schmaus, 2020. "A generic business process model for conducting microsimulation studies," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 191-211, August.
    8. Gijs Dekkers & Ekaterina Tarantchenko & Karel Van den Bosch, 2019. "Working Paper 03-19 - Medium-term projection for Belgium of the at-risk-of-poverty and social exclusion indicators based on EU-SILC [Working Paper 03-19 - Prévisions à moyen terme des indicateurs d," Working Papers 1903, Federal Planning Bureau, Belgium.
    9. Didier Blanchet & Cyrille Hagneré & François Legendre & Florence Thibault, 2015. "Introduction. Microsimulations statique et dynamique appliquées aux politiques fiscales et sociales : modèles et méthodes," Économie et Statistique, Programme National Persée, vol. 481(1), pages 5-30.

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    More about this item

    Keywords

    dynamic ageing; static ageing; microsimulation;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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