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Penalized Small Area Models for the Combination of Unit- and Area-level Data

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  • Jan Pablo Burgard
  • Joscha Krause
  • Ralf Münnich

Abstract

The joint usage of unit- and area-level data for model-based small area estimation is investigated. The combination of levels within a single model encloses a variety of methodological problems. Firstly, it implies a critical decrease in degrees of freedom due to more model parameters that need to be estimated. This may destabilize model predictions in the presence of small samples. Secondly, unit- and area-level data has different distributional characteristics in terms of dispersion patterns and correlation structure. Thirdly, unit- and area-level data is usually subject to different kinds of measurement errors. We propose a multi-level model with level-specific penalization to overcome these issues and use unit- and area-level data jointly for model-based small area estimation. An application is provided on the example of regional health measurement in Germany. We combine health survey data on the unit-level and aggregated micro census records on the area-level to estimate hypertension prevalence.

Suggested Citation

  • Jan Pablo Burgard & Joscha Krause & Ralf Münnich, 2019. "Penalized Small Area Models for the Combination of Unit- and Area-level Data," Research Papers in Economics 2019-05, University of Trier, Department of Economics.
    • Handle: RePEc:trr:wpaper:201905
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    References listed on IDEAS

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    Keywords

    disease mapping; multi-level model; multi-source estimation; penalization; stochastic gradient descent;
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