Jan Pablo Burgard studied economics at Trier University and made his PhD in statistics covering topics in survey statistics and small area estimation. He habilitated in statistics and econometrics and is now visiting professor at the Freie Universität in Berlin. His main research directions are survey statistics, applied statistics and statistical learning. One methodological framework where all of these fields are necessary is microsimulations -- the topic of today's talk.

Spatial dynamic microsimulations allow for the multivariate analysis of complex systems with geographic segmentation. A synthetic replica of the system is stochastically projected into future periods using micro-level transition probabilities. These should accurately represent the dynamics of the system to allow for reliable simulation outcomes. In practice, transition probabilities are unknown and must be estimated from suitable survey data. This can be challenging when the dynamics vary locally. Survey data often lacks in regional detail due to confidentiality restrictions and limited sampling resources. In that case, transition probability estimates may misrepresent regional dynamics due to insufficient local observations and coverage problems. The simulation process subsequently fails to provide an authentic evolution of the system. A constrained maximum likelihood approach for probability alignment to solve these issues is proposed. It accounts for regional heterogeneity in transition dynamics through the consideration of external benchmarks from administrative records. It is proven that the method is consistent. A parametric bootstrap for uncertainty estimation is presented. Simulation experiments are conducted to compare the approach with an existing method for probability alignment. Furthermore, an empirical application to labor force estimation based on the German Microcensus is provided.