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Small Area-Statistik: Methoden und Anwendungen

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  • Ralf Münnich
  • Jan Burgard
  • Martin Vogt

Abstract

Modern household surveys increasingly provide information on subgroups as defined by content or regions. This kind of information, in general, is gained from censuses every ten years. Within the current European census round, some countries have decided to implement new methods which do not rely on a complete enumeration of the population. Switzerland and Germany, for example, are applying a register-assisted census. An exploitation of the register of residents is enriched with information gained from an additional sample. This sample also furnishes possible statistical corrections of the register. This change of paradigm in official statistics urges for adequate statistical methods. In a register-assisted census, additionally to efficient estimates at national level, reliable regional estimates are required. However, the disaggregation may result in very low sample sizes for some of the areas of interest. Whilst classical design-based methods will not produce reliable estimates for these areas, modern model-based small area methods may improve the quality of the estimates by far. The present work focuses on illustrating the small area estimation concepts and methods by two examples of recent research on register-assisted censuses. Additionally to two basic small area models, various recent extensions will be discussed. The successful application of these methods is of crucial importance for obtaining reliable regionalized statistics. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Ralf Münnich & Jan Burgard & Martin Vogt, 2013. "Small Area-Statistik: Methoden und Anwendungen," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 6(3), pages 149-191, March.
  • Handle: RePEc:spr:astaws:v:6:y:2013:i:3:p:149-191
    DOI: 10.1007/s11943-013-0126-1
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    References listed on IDEAS

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    4. Martin Vogt & Ralf Munnich, 2009. "On the existence of a posterior distribution for spatial mixed models with binomial responses," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 201-209.
    5. Siegfried Gabler & Matthias Ganninger & Ralf Münnich, 2012. "Optimal allocation of the sample size to strata under box constraints," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(2), pages 151-161, February.
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    10. Peter Hall & Tapabrata Maiti, 2006. "On parametric bootstrap methods for small area prediction," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 221-238, April.
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    Cited by:

    1. Ralf Thomas Münnich, 2016. "Vorwort des Herausgebers," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(4), pages 197-203, December.
    2. Jan Pablo Burgard & Ralf Münnich & Martin Rupp, 2019. "A Generalized Calibration Approach Ensuring Coherent Estimates with Small Area Constraints," Research Papers in Economics 2019-10, University of Trier, Department of Economics.
    3. Ralf Münnich & Jan Pablo Burgard, 2015. "SAE teaching using simulations," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(4), pages 603-610, December.
    4. Ulrich Rendtel, 2014. "Vom potenziellen Datenangreifer zum zertifizierten Wissenschaftler – Für eine Neugestaltung des Wissenschaftsprivilegs beim Datenzugang," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 8(4), pages 183-197, November.
    5. Friedrich, Ulf & Münnich, Ralf & de Vries, Sven & Wagner, Matthias, 2015. "Fast integer-valued algorithms for optimal allocations under constraints in stratified sampling," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 1-12.
    6. Jan Pablo Burgard & Ralf Münnich, 2015. "Sae Teaching Using Simulations," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 603-610, December.
    7. Jan Breitkreuz & Gabriela Brückner & Jan Pablo Burgard & Joscha Krause & Ralf Münnich & Helmut Schröder & Katrin Schüssel, 2019. "Schätzung kleinräumiger Krankheitshäufigkeiten für die deutsche Bevölkerung anhand von Routinedaten am Beispiel von Typ-2-Diabetes [Estimation of regional diabetes type 2 prevalence in the German p," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 13(1), pages 35-72, April.
    8. Ralf Münnich, 2013. "Vorwort des Herausgebers," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 7(3), pages 101-103, December.
    9. Ann-Kristin Kreutzmann, 2018. "Estimation of sample quantiles: challenges and issues in the context of income and wealth distributions [Die Schätzung von Quantilen: Herausforderungen und Probleme im Kontext von Einkommens- und V," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 12(3), pages 245-270, December.
    10. Saeideh Kamgar & Florian Meinfelder & Ralf Münnich & Hamidreza Navvabpour, 2020. "Estimation within the new integrated system of household surveys in Germany," Statistical Papers, Springer, vol. 61(5), pages 2091-2117, October.
    11. Thomas Zimmermann, 2019. "Einsatzmöglichkeiten von Small Area-Verfahren bei Kohortenschätzungen im Zensus 2021 [Applicablity of small area estimation methods for demographic cohorts in the Census 2021]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 13(2), pages 157-177, September.
    12. Charlotte Articus & Jan Pablo Burgard, 2014. "A Finite Mixture Fay Herriot-type model for estimating regional rental prices in Germany," Research Papers in Economics 2014-14, University of Trier, Department of Economics.
    13. Ralf Münnich & Julian Wagner & Joachim Hill & Johannes Stoffels & Henning Buddenbaum & Thomas Udelhoven, 2016. "Schätzung von Holzvorräten unter Verwendung von Fernerkundungsdaten [Estimation of timber reserves using remote sensing data]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(2), pages 95-112, October.
    14. Sebastian Dräger & Johannes Kopp & Ralf Münnich & Simon Schmaus, 2022. "Die zukünftige Entwicklung der Grundschulversorgung im Kontext ausgewählter Wanderungsszenarien [The future development of primary school demand in the context of selected migration scenarios]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(1), pages 51-77, March.
    15. Burgard Jan Pablo & Münnich Ralf, 2015. "Sae Teaching Using Simulations," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 603-610, December.
    16. Ralf Münnich, 2013. "Vorwort des Herausgebers," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 6(3), pages 83-85, March.
    17. Ulrich Rendtel & Stefan Liebig & Reinhard Meister & Gert G. Wagner & Sabine Zinn, 2021. "Die Erforschung der Dynamik der Corona-Pandemie in Deutschland: Survey-Konzepte und eine exemplarische Umsetzung mit dem Sozio-oekonomischen Panel (SOEP) [The research on the dynamics of the Corona," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 15(3), pages 155-196, December.
    18. Ralf Münnich & Siegfried Gabler & Christian Bruch & Jan Pablo Burgard & Tobias Enderle & Jan-Philipp Kolb & Thomas Zimmermann, 2015. "Tabellenauswertungen im Zensus unter Berücksichtigung fehlender Werte," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 9(3), pages 269-304, December.

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    More about this item

    Keywords

    Regionalstatistik; Synthetische Schätzung; Designbasierte Schätzung; Zusammengesetzte Schätzung; C83; C31; C13; C15; Regional statistics; Synthetic estimation; Design-based estimation; Composite estimation;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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