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On weights in dynamic-ageing microsimulation models

Author

Listed:
  • Gijs Dekkers

    (Federal Planning Bureau, CeSO University of Leuven, and CEPS/INSTEAD)

  • Richard Cumpston

    (Research School of Finance, Actuarial Studies & Applied Statistics Australian National University)

Abstract

A dynamic model with cross-sectional dynamic ageing builds up complete synthetic life histories for each individual, starting from a survey dataset or an administrative dataset. Many of these datasets include weights. This is a problem for dynamic microsimulation models, since the most obvious solution, expanding the dataset, is not always advisable or even possible. This short paper presents an alternative method to use weights in dynamic microsimulation models with dynamic ageing. This approach treats the weighting variable as just another variable in the model, and the weights are only used after simulation to derive weighted simulation results. Tests using Australian data confirm that weighted variables give sensible results, with reductions in runtimes and memory requirements.

Suggested Citation

  • Gijs Dekkers & Richard Cumpston, 2012. "On weights in dynamic-ageing microsimulation models," International Journal of Microsimulation, International Microsimulation Association, vol. 5(2), pages 59-65.
    • Handle: RePEc:ijm:journl:v:5:y:2012:i:2:p:59-65
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    References listed on IDEAS

    as
    1. Jinjing Li & Cathal O'Donoghue, 2014. "Evaluating Binary Alignment Methods in Microsimulation Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(1), pages 1-15.
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    Cited by:

    1. Konstantins Benkovskis & Ludmila Fadejeva & Anna Pluta & Anna Zasova, 2023. "Keeping the best of two worlds: Linking CGE and microsimulation models for policy analysis," Working Papers 2023/01, Latvijas Banka.
    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. 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.
    4. Alessandra Caretta & Sara Flisi & Cecilia Frale & Michele Raitano & Simone Tedeschi, 2013. "T-DYMM : the treasury dynamic microsimulation model of the Italian pension system," Working Papers 11, Department of the Treasury, Ministry of the Economy and of Finance.
    5. 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.
    6. 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.

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    Keywords

    sample weights; dynamic microsimulation;

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