Beitrag in einem Sammelband

Population forecasting via microsimulation: the software design of the MicMac project

Gampe, J., Zinn, S., Willekens, F. J., van den Gaag, N.

In: EUROSTAT (Hrsg.): Work session on demographic projections: Bucharest, 10-12 October 2007, 229-233 (2007)
Eurostat: methodologies and working papers; theme: population and social conditions
Luxembourg: Office for Official Publications of the European Communities.

ISBN 978-92-79-04759-6


This paper describes design considerations and the general layout of the microsimulation software of the project ‘MicMac - Bridging the micro-macro gap in population forecasting’, which is funded by the European Commission under the 6th Framework Programme. In microsimulation life-courses of individuals are projected by randomly drawing their trajectories from a stochastic model, which portrays the propensity for individual transitions between relevant demographic states during life (Willekens, 2005). These simulated life-courses are collected in a virtual population and inference on future population development can be made by analysing this virtual population. If the underlying model realistically describes individual behaviour, then rich and detailed future population characteristics can be derived from the analysis of the aggregated simulated life-courses. This procedure has several key ingredients: A stochastic model that is able to characterize individual behaviour over the life-course in settings that can be rather complex. Data sources, statistical models, and corresponding estimating procedures that allow to derive the empirical input for the microsimulation, that is, the estimated transition rates. And software that combines the input, allows to incorporate assumptions about future behavioural and institutional changes easily, performs the actual life-course simulations, and provides the simulation results in a format that will allow detailed further analysis. The microsimulation software that is developed as part of the MicMac-project shall serve all these purposes. It will contain a pre-processor to facilitate the estimation of relevant transition rates from data. Then the so called Mic-core will perform the simulation according to the underlying multistate model. Finally, a so called postprocessor will provide tools for presentation of results. The following section will first summarize some general considerations for the software design. Then we will briefly describe the underlying multistate model, followed by a description of the Mic-core. A summary of the current state of development, which is still in progress, and an outlook on features still to be implemented will conclude the paper.