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Sensitivity Analysis for Dynamic Microsimulation Models

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  • Jan Pablo Burgard
  • Simon Schmaus

Abstract

Microsimulations usually contain various transition processes such as births and deaths, relocations, and change in household characteristics. The estimation, organisation, and implementation of these processes can have a substantial impact on the simulation outcomes. We propose to evaluate a dynamic discrete-time stochastic microsimulation using the concept of variance based sensitivity analysis which is well established for indicator assessment in survey statistics. This method is suitable for measuring and comparing the impact of different input factors on the output, even when analytical methods – given the complexity in a dynamic framework – cannot be applied.

Suggested Citation

  • Jan Pablo Burgard & Simon Schmaus, 2019. "Sensitivity Analysis for Dynamic Microsimulation Models," Research Papers in Economics 2019-15, University of Trier, Department of Economics.
    • Handle: RePEc:trr:wpaper:201915
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    File URL: http://www.uni-trier.de/fileadmin/fb4/prof/VWL/EWF/Research_Papers/2019-15.pdf
    File Function: First version, 2019
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    References listed on IDEAS

    as
    1. Carolyn M. Rutter & Alan M. Zaslavsky & Eric J. Feuer, 2011. "Dynamic Microsimulation Models for Health Outcomes," Medical Decision Making, , vol. 31(1), pages 10-18, January.
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    Cited by:

    1. 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.

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