Model Recalibration for Regional Bias Reduction in Dynamic Microsimulations

Jan Weymeirsch, Julian Ernst and Ralf Münnich ()
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Jan Weymeirsch: Economic and Social Statistics, Trier University, Universitätsring 15, 54296 Trier, Germany
Julian Ernst: Economic and Social Statistics, Trier University, Universitätsring 15, 54296 Trier, Germany
Ralf Münnich: Economic and Social Statistics, Trier University, Universitätsring 15, 54296 Trier, Germany

Mathematics, 2024, vol. 12, issue 10, 1-25

Abstract: Dynamic microsimulations are tools to stochastically project (synthetic) microdata into the future. In spatial microsimulations, regional discrepancies are of particular interest and must be considered accordingly. In practice, the probabilities for state changes are unknown and must be estimated, usually from survey data. However, estimating such models on the regional level is often not feasible due to limited sample size and lack of geographic information. Simply applying the model estimated at the national level to all geographies leads to biased state transitions due to regional differences in level and distribution. In this paper, we introduce a model-based alignment method to adapt predicted probabilities obtained from a nationally estimated model to subregions by integrating known marginal distributions to re-introduce regional heterogeneity and create more realistic trajectories, particularly in small areas. We show that the model-adjusted transition probabilities can capture region-specific patterns and lead to improved projections. Our findings are useful to researchers who want to harmonise model outputs with external information, in particular for the field of microsimulation.

Keywords: microsimulation; alignment; bias reduction; model calibration; fertility; population projection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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