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Faculty of Social Sciences, Economics, and Business Administration

Chair of Statistics and Econometrics

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  9. New paper in Journal of the Royal Statistical Society Series C

New paper in Journal of the Royal Statistical Society Series C

Nora Würz, Timo Schmid and co-authors use random forests under access to limited auxiliary information.

Analysing opportunity cost of care work using mixed effects random forests under aggregated auxiliary data

Krennmair, P.; Würz, N.; Schmid, T. 

Abstract: Evidence-based policy-making requires reliable, spatially disaggregated indicators. The framework of mixed effects random forests leverages the advantages of random forests and hierarchical data in small area estimation. These methods require typically access to auxiliary information on population level, which is a strong limitation for practitioners. In contrast, our proposed method—for point and uncertainty estimation—abstains from access to unit-level population data but adaptively incorporates aggregated auxiliary information through calibration weights. We demonstrate its usage for estimating opportunity cost of care work for Germany from the Socio-Economic Panel and census aggregates. Simulation studies evaluate our proposed method.

Patrick Krennmair, Nora Würz & Timo Schmid (2025) Analysing opportunity cost of care work using mixed effects random forests under aggregated auxiliary data, Journal of the Royal Statistical Society Series C, DOI: https://doi.org/10.1093/jrsssc/qlaf031