A generic business process model for conducting microsimulation studies


Share / Export Citation / Email / Print / Text size:

Statistics in Transition New Series

Polish Statistical Association

Central Statistical Office of Poland

Subject: Economics, Statistics & Probability


ISSN: 1234-7655
eISSN: 2450-0291





Volume / Issue / page

Related articles

VOLUME 21 , ISSUE 4 (August 2020) > List of articles

Special Issue

A generic business process model for conducting microsimulation studies

Jan Pablo Burgard / Hanna Dieckmann / Joscha Krause / Hariolf Merkle / Ralf Münnich / Kristina M. Neufang / Simon Schmaus

Keywords : multi-source analysis, multivariate modeling, social simulation, synthetic data generation

Citation Information : Statistics in Transition New Series. Volume 21, Issue 4, Pages 191-211, DOI: https://doi.org/10.21307/stattrans-2020-038

License : (CC BY-NC-ND 4.0)

Received Date : 31-January-2020 / Accepted: 30-June-2020 / Published Online: 15-September-2020



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.

Content not available PDF Share



ALFONS, A., FILZMOSER, P., HULLIGER, B., KOLB, J. P., KRAFT, S., MUNNICH, ¨ R., TEMPL, M., (2011a). Synthetic data generation of SILC data. AMELI Research Project Report WP6-D6, 2.

ALFONS, A., KRAFT, S., TEMPL, M., FILZMOSER, P., (2011b). Simulation of closeto-reality population data for household surveys with application to EU-SILC. Statistical Methods & Applications, 20(3), pp. 383–407.

BELANGER, A., SABOURIN, P., (2017). Microsimulation and Population Dynamics: ´ An Introduction to Modgen 12. Springer.

BOURGUIGNON, F., SPADARO, A., (2006). Microsimulation as a tool for evaluating redistribution policies. The Journal of Economic Inequality, Vol. 4, pp. 77–106.

BURGARD, J. P., KRAUSE, J., MERKLE, H., MUNNICH, R., SCHMAUS, S., (2019a). ¨ Conducting a dynamic microsimulation for care research: Data generation, transition probabilities and sensitivity analysis. In Stochastic Models, Statistics and Their Applications. A. Steland, E. Rafajlowicz and O. Okhrin (eds.) Cham: Springer International Publishing, pp. 269–290.

BURGARD, J. P., KRAUSE, J., SCHMAUS, S., (2019b). Estimation of regional transition probabilities for spatial dynamic microsimulations from survey data lacking in regional detail. Research Papers in Economics, No. 12/19, Trier University.

BURGARD, J. P., KRAUSE, J., MERKLE, H., MUNNICH, R., SCHMAUS, S., (2020). ¨ Dynamische Mikrosimulationen zur Analyse und Planung regionaler Versorgungsstrukturen in der Pflege. In Mikrosimulationen - Methodische Grundlagen und ausgew¨ahlte Anwendungsfelder. M. Hannappel and J. Kopp (eds.) Wiesbaden: Springer VS, pp. 283–313.

BURGARD, J. P., MUNNICH, R. T., RUPP, M., (2019c). A generalized calibration ¨ approach ensuring coherent estimates with small area constraints (No. 10/19). Research Papers in Economics.

BURGARD, J. P., SCHMAUS, S., (2019). Sensitivity analysis for dynamic microsimulation models (No. 15/19). Research Papers in Economics, Trier University.

CHEN, J., QIN, J., (1993). Empirical likelihood estimation for finite populations and the effective usage of auxiliary information. Biometrika, Vol. 80, pp. 107–116.

COX, D. R., (1972). Regression models and life-tables. Journal of the Royal Statistical Society. Series B (Methodological), Vol. 9, pp. 439–455.

DE MENTEN, G., DEKKERS, G., BRYON, G., LIEGEOIS, P., O’DONOGHUE, C., (2014). Liam2: a new open source development tool for discrete-time dynamic microsimulation models. Journal of Artificial Societies and Social Simulation, Vol. 17, p. 9.

DEKKERS, G., (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, Vol. 8, pp. 97–109.

DEKKERS, G., CUMPSTON, R., (2012). On weights in dynamic-ageing microsimulation models. The International Journal of Microsimulation, Vol. 5(2), pp. 59–65.

DEVILLE, J., SARNDAL, C., (1992). Calibration estimators in survey sampling. ¨ Journal ofthe American Statistical Association, 87, pp. 376–382.

DRECHSLER, J., (2011). Synthetic Datasets for Statistical Disclosure Control: Theory and Implementation. Vol. 201. Springer Science & Business Media.

EUROPEAN COMMISSION, (2008). Commission Recommendation of 10 April 2008 on the management of intellectual property in knowledge transfer activities and Code of Practice for universities and other public research organisations (notified under document number C(2008) 1329) (Text with EEA relevance). Official Journal of the European Union (L 146), Vol. 51, pp. 19–24.

FAVREAULT, M. M., SMITH, K. E., JOHNSON, R. W., (2015). The dynamic simulation of income model (DYNASIM). Research Report at Urban Institute, Washington DC.

GOEDEME, T., VAN DEN BOSCH, K., SALANAUSKAITE, L., VERBIST, G., (2013). Testing the statistical significance of microsimulation results: A plea. International Journal of Microsimulation, 6(3), pp. 50–77.

GREENE, W. H., (2003). Econometric analysis (5 ed.) New Jersey: Prentice Hall.

HANNAPPEL, M., TROITZSCH, K. G., (2015). Mikrosimulationsmodelle. In N.Braun, N.J.Saam (eds): Modellbildung und Simulation in den Sozialwissenschaften, (pp. 455–489). Springer VS, Wiesbaden.

HAZIZA, D., BEAUMONT, J. F., (2017). Construction of weights in surveys: A review. Statistical Science, Vol. 32, pp. 206–226.

HUANG, Z.; WILLIAMSON, P., (2001). A Comparison of Synthetic Reconstruction and Combinatorial Optimisation Approaches to the Creation of Small-Area Microdata. University of Liverpool. Department of Geography. Working Paper 2001/2.

KLEIBER, C., ZEILEIS, A., (2013). Reproducible econometric simulations. Journal of Econometric Methods, Vol. 2, pp. 89–99.

LAPPO, S., (2015). Uncertainty in microsimulation, Master’s Thesis, University of Helsinki.

LI, J., O’DONOGHUE, C., (2013). A survey of dynamic microsimulation models. Uses, model structure and methodology. International Journal of Microsimulation, Vol. 6, pp. 3–55.

LI, J., O’DONOGHUE, C., (2014). Evaluating binary alignment methods in microsimulation models. Journal of Artificial Societies and Social Simulation, Vol. 17, pp. 1–15.

LOVELACE, R., DUMONT, M., (2016). Spatial microsimulation with R. Chapman and Hall/CRC.

MANNION, O., LAY-YEE, R., WRAPSON, W., DAVIS, P., PEARSON, J., (2012). JAMSIM: A microsimulation modelling policy tool. Journal of Artificial Societies and Social Simulation, Vol. 15, p. 8.

MARKHAM, F., YOUNG, M., DORAN, B., (2017). Improving spatial microsimulation estimates of health outcomes by including geographic indicators of health behaviour: The example of problem gambling. Health & Place, Vol. 46, pp. 29–36.

MCCULLAGH, P., NELDER, J. A., (1989). Generalized linear models (2 ed.), Vol. 37 of Monographs on Statistics and Applied Probability London: Chapman and Hall.

MUENNIG, P.A., MOHIT, B., WU, J., JIA, H., ROSEN, Z., (2016). Coest effectiveness of the earned income tax credit as health policy investiment. American Journal of Preventive Medicine, Vol. 51(6), pp. 874–881.

MÜNNICH R, SCHÜRLE J., (2003). On the simulation of complex universes in the ¨ case of applying the GermanMicrocensus. DACSEIS research paper series No. 4, University of T¨ubingen.

MURTHY, D., (2012). Towards a sociological understanding of social media: theorizing Twitter. Sociology, Vol. 46(6), pp. 1–15.

O’DONOGHUE, C., (2001). Dynamic Microsimulation: A Methodological Survey. Brazilian Electronic Journal of Economics, Vol. 4, p. 77.

O’DONOGHUE, C., LENNON, J., HYNES, S., (2009). The Life-cycle Income Analysis Model (LIAM): a study of a flexible dynamic microsimulation modelling computing framework. International Journal of Microsimulation, Vol. 2, pp. 16–31.

O’DONOGHUE, C., DEKKERS, G., (2018). Increasing the impact of dynamic microsimulation modelling. International Journal of Microsimulation, Vol. 11, pp. 61–96.

ORCUTT, G. H., (1957). A new type of socio-economic system. The review of economics and statistics, 58, pp. 116–123.

PETRIK, O., ADNAN, M., BASAK, K., BEN-AKIVA, M., (2018). Uncertainty analysis of an activity-based microsimulation model for Singapore. Future Generation Computer Systems.

PICHON-RIVIERE, A., AUGUSTOVSKI, F., BARDACH, A., COLANTONIO, L., (2011). Development and validation of a microsimulation economic model to evaluate the disease burden associated with smoking and the cost-effectiveness of tobacco control interventions in Latin America. Value in Health, Vol. 14, S51–S59.

RAHMAN, A., HARDING, A., (2017). Small area estimation and microsimulation modeling. Boca Raton: CRC Press, Taylor & Francis Group.

SALTELLI, A., RATTO, M., TERRY, A., CAMPOLOGNO, F., CARIBONI, J., GATELLI, D., SAISANA, M., TARANTOLA, S., (2008). Global sensitivity analysis. The Primer. Chichester: John Wiley & Sons.

SÄRNDAL, C.-E., (2007). The calibration approach in survey theory and practice. ¨ Survey Methodology, Vol. 33, pp. 99–119.

SCHAFER, J.L., GRAHAM, J. W., (2002). Missing data: Our view of the state of the art. Psychological Methods, Vol. 7, pp. 147–177.

SCHAICH, E., MUNNICH, R., (2001). Mathematische Statistik für Ökonomen. Vahlen.

SHARIF, B., KOPEC, J. A., WONG, H., FINES, P., SAYRE, E. C., LIU, R. R., WOLFSON, M. C., (2012). Uncertainty analysis in population-based disease microsimulation models. Epidemiology Research International, 2012.

SHARIF, B., WONG, H., ANIS, A. H., KOPEC, J. A., (2017). A practical ANOVA approach for uncertainty analysis in population-based disease microsimulation models. Value in Health, Vol. 20(4), pp. 710–717.

SMITH, D.M., CLARKE, G.P., HARLAND, K., (2009). Improving the synthetic data generation process in spatial microsimulation models. Environment and Planning A: Economy and Space, Vol. 41, pp. 1251–1268.

SPIELAUER, M., (2006). The “Life Course” model, a competing risk cohort microsimulation model: source code and basic concepts of the generic microsimulation programming language Modgen, MPIDR WORKING PAPER 2006–046.

SPIELAUER, M., (2009). Microsimulation approaches. Technical Report, Statistics Canada, Modeling Division.

SPIELAUER, M., (2011). What is Social Science Microsimulation? Social Science Computer Review, Vol. 29, pp. 9–20.

STEPHENSEN, P., (2016). Logit scaling: A general method for alignment in microsimulation models. International Journal of Microsimulation, Vol. 9, pp. 89–102.

SUTHERLAND, H., FIGARI, F., (2013). EUROMOD: the European Union tax-benefit microsimulation model. International Journal of Microsimulation, Vol. 6, pp. 4–26.

TANTON, R., (2018). Spatial microsimulation: Developments and potential future directions. International Journal of Microsimulation, Vol. 11(1), pp. 143–161.

TANTON, R., WILLIAMSON, P., HARDING, A., (2014). Comparing two methods of reweighting a survey file to small area data. International Journal of Microsimulation, 7(1), pp. 76–99.

UNECE, (2013). Generic statistical business process model. Version 5.0 – December 2013. The United Nations Economic Commission for Europe (UNECE). URL: http://www1.unece.org/stat/platform/display/GSBPM/GSBPM+v5.0.

VAN IMHOFF, E., POST, W., (1998). Microsimulation methods for population projection. Population: An English Selection, Vol. 10, pp. 97–138.

WILLEKENS, F., (2017). Continuous-time microsimulation in longitudinal analysis. In New Frontiers in Microsimulation Modelling. A. Zaidi, A. Harding and P. Williamson (eds.), Routledge, pp. 413–436.

WILLIAMSON, P., (2013). An evaluation of two synthetic small-area microdata simulation methodologies: Synthetic reconstruction and combinatorial optimisation. In In Tanton and Edwards (eds): Spatial microsimulation: A reference guide for users. Springer, Dordrecht.