A generic business process model for conducting microsimulation studies

Burgard Jan Pablo (), Dieckmann Hanna (), Krause Joscha (), Merkle Hariolf (), Münnich Ralf (), Neufang Kristina M. () and Schmaus Simon ()
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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 .

Statistics in Transition New Series, 2020, vol. 21, issue 4, 191-211

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.

Keywords: multi-source analysis; multivariate modeling; social simulation; synthetic data generation (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:21:y:2020:i:4:p:191-211:n:3

DOI: 10.21307/stattrans-2020-038

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