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Problems and Prospects for Dynamic Microsimulation: A review and lessons for APPSIM

Author

Listed:
  • Rebecca Cassells

    (NATSEM, University of Canberra)

  • Ann Harding

    (NATSEM, University of Canberra)

  • Simon Kelly

    (NATSEM, University of Canberra)

Abstract

This paper has been prepared as the first in a series of papers associated with the development of the Australian Population and Policy Simulation Model (APPSIM). The APPSIM dynamic population microsimulation model is being developed as part of an Australian Research Council (ARC) Linkage grant (LP0562493), and will be used by Commonwealth Government policy makers and other analysts to assess the social and fiscal policy implications of Australia's ageing population. This paper reviews progress nationally and internationally on the construction of dynamic population microsimulation models and considers the lessons that might be taken from that earlier experience for the construction of the APPSIM model.

Suggested Citation

  • Rebecca Cassells & Ann Harding & Simon Kelly, 2006. "Problems and Prospects for Dynamic Microsimulation: A review and lessons for APPSIM," NATSEM Working Paper Series 63, University of Canberra, National Centre for Social and Economic Modelling.
    • Handle: RePEc:cba:wpaper:63
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    References listed on IDEAS

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    Cited by:

    1. Jinjing Li & Cathal O'Donoghue, 2012. "Simulating Histories within Dynamic Microsimulation Models," International Journal of Microsimulation, International Microsimulation Association, vol. 5(1), pages 52-76.
    2. Jan-Maarten van Sonsbeek & j.m.van.sonsbeek@vu.nl, 2011. "Micro simulations on the effects of ageing-related policy measures: The Social Affairs Department of the Netherlands Ageing and Pensions Model," International Journal of Microsimulation, International Microsimulation Association, vol. 4(1), pages 72-99.
    3. Martin Spielauer, 2007. "Dynamic microsimulation of health care demand, health care finance and the economic impact of health behaviours: survey and review," International Journal of Microsimulation, International Microsimulation Association, vol. 1(1), pages 35-53.
    4. 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).
    5. Nicholas-James Clavet & Jean-Yves Duclos & Bernard Fortin & Steeve Marchand, 2012. "Le Québec, 2004-2030 : une analyse de micro-simulation," CIRANO Project Reports 2012rp-16, CIRANO.
    6. Martin Spielauer & Thomas Horvath & Marian Fink, 2020. "microWELT: A Dynamic Microsimulation Model for the Study of Welfare Transfer Flows in Ageing Societies from a Comparative Welfare State Perspective," WIFO Working Papers 609, WIFO.
    7. 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.
    8. Alessandro Bucciol & Laura Cavalli & Igor Fedotenkov & Paolo Pertile & Veronica Polin & Nicola Sartor & Alessandro Sommacal, 2014. "A large scale OLG model for France, Italy and Sweden: assessing the interpersonal and intrapersonal redistributive effects of public policies," Working Papers 07/2014, University of Verona, Department of Economics.
    9. 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.
    10. 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.
    11. Li Tan & Cory Koedel, 2019. "The Effects of Differential Income Replacement and Mortality on U.S. Social Security Redistribution," Southern Economic Journal, John Wiley & Sons, vol. 86(2), pages 613-637, October.

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • I1 - Health, Education, and Welfare - - Health

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