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A generic business process model for conducting microsimulation studies

Author

Listed:
  • Burgard Jan Pablo

    (Trier University, Department of Economic and Social Statistics, Trier, ; Germany .)

  • Dieckmann Hanna

    (Trier University, Department of Economic and Social Statistics, Trier, ; Germany .)

  • Krause Joscha

    (Trier University, Department of Economic and Social Statistics, Trier, ; Germany .)

  • Merkle Hariolf

    (Trier University, Department of Economic and Social Statistics, Trier, ; Germany .)

  • Münnich Ralf

    (Trier University, Department of Economic and Social Statistics, Trier, ; Germany .)

  • Neufang Kristina M.

    (Trier University, Department of Economic and Social Statistics, Trier, ; Germany .)

  • Schmaus Simon

    (Trier University, Department of Economic and Social Statistics, Trier, ; Germany .)

Abstract

Microsimulations make use of quantitative methods to analyze complex phenomena in populations. They allow modeling socioeconomic systems based on micro-level units such as individuals, households, or institutional entities. However, conducting a microsimulation study can be challenging. It often requires the choice of appropriate data sources, micro-level modeling of multivariate processes, and the sound analysis of their outcomes. These work stages have to be conducted carefully to obtain reliable results. We present a generic business process model for conducting microsimulation studies based on an international statistics process model. This simplifies the comprehensive understanding of dynamic microsimulation models. A nine-step procedure that covers all relevant work stages from data selection to output analysis is presented. Further, we address technical problems that typically occur in the process and provide sketches as well as references of solutions.

Suggested Citation

  • 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.
    • Handle: RePEc:vrs:stintr:v:21:y:2020:i:4:p:191-211:n:3
    DOI: 10.21307/stattrans-2020-038
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    References listed on IDEAS

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    1. François Bourguignon & Amedeo Spadaro, 2006. "Microsimulation as a tool for evaluating redistribution policies," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 4(1), pages 77-106, April.
    2. Robert Tanton & Paul Williamson & Ann Harding, 2014. "Comparing Two Methods of Reweighting a Survey File to Small Area Data," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 76-99.
    3. 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.
    4. Kleiber Christian & Zeileis Achim, 2013. "Reproducible Econometric Simulations," Journal of Econometric Methods, De Gruyter, vol. 2(1), pages 89-99, July.
    5. Jan Pablo Burgard & Ralf Münnich & Martin Rupp, 2019. "A Generalized Calibration Approach Ensuring Coherent Estimates with Small Area Constraints," Research Papers in Economics 2019-10, University of Trier, Department of Economics.
    6. 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.
    7. 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.
    8. Andreas Alfons & Stefan Kraft & Matthias Templ & Peter Filzmoser, 2011. "Simulation of close-to-reality population data for household surveys with application to EU-SILC," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(3), pages 383-407, August.
    9. Martin Spielauer, 2006. "The "LifeCourse" model, a competing risk cohort microsimulation model: source code and basic concepts of the generic microsimulation programming language Modgen," MPIDR Working Papers WP-2006-046, Max Planck Institute for Demographic Research, Rostock, Germany.
    10. Gaëtan de Menten & Gijs Dekkers & Geert Bryon & Philippe Liégeois & Cathal O'Donoghue, 2014. "LIAM2: a New Open Source Development Tool for Discrete-Time Dynamic Microsimulation Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(3), pages 1-9.
    11. 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.
    12. 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.
    13. Peter Stephensen, 2016. "Logit Scaling: A General Method for Alignment in Microsimulation models," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 89-102.
    14. Jan Pablo Burgard & Joscha Krause & Simon Schmaus, 2019. "Estimation of Regional Transition Probabilities for Spatial Dynamic Microsimulations from Survey Data Lacking in Regional Detail," Research Papers in Economics 2019-12, University of Trier, Department of Economics.
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