Project Details
Projekt
Efficiency and uncertainty measurement in microsimulations
Applicant
Professor Dr. Ralf Münnich
Subject Area
Empirical Social Research
Term
since 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 316511172
The MikroSim base population is a (partially) synthetic, close-to-reality population of Germany, containing more than 80 million individuals in approximately 40 million households in the year 2011. Using individual transition probabilities, the whole population can be projected into the future. The projection of such a large number of individual observations enables detailed analyses, i.e.\ analyses with a close regional or contextual focus. At the same time, it causes an enormous computational effort. A more wide-spread use of this important analysis tool can be fostered by reducing the necessary computation time.The complexity of dynamic microsimulation models, that originates from the nature of the investigated research questions, leads to a multitude of potential influences on the precision of analysis results. While it is common practice in the social sciences to state measures of precision with estimators, like confidence intervals, this is typically neglected in the context of microsimulations. The reasons for this are the lack of available methods for the systematic measurement and combination of all relevant sources of uncertainty on the one hand, and the additional computational burden associated with such a measurement on the other hand. However, in order to be able to judge the validity of results, the measurement of their precision is also needed in microsimulations.Therefore, the main research focus within this project is on efficiency and uncertainty measurement in microsimulations which are closely related to each other. First, the potential for efficiency improvements in microsimulations by using samples from the base population for projections is investigated. Special attention is paid to the suitability of different complex sampling designs for the projection of target values on different levels of regional and content-related granularity. Then, the diverse sources of uncertainty in the simulation process are identified. Using different statistical methods, these sources are implemented into the simulation structure as an aggregated uncertainty process. The latter can then be used to construct quasi confidence intervals for simulation results that facilitate the judgement of their validity.The methods that are developed within this project are directly applied to microsimulations of the other projects within the Research Unit. Additionally, they are provided to researchers in an application-oriented guideline and implemented as a tool within the prototype of a simulation data centre.
DFG Programme
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