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Einsatz von Machine-Learning-Verfahren in amtlichen Unternehmensstatistiken
[Use of machine learning in official business statistics]

Author

Listed:
  • Florian Dumpert

    (Universität Bayreuth)

  • Martin Beck

    (Statistisches Bundesamt)

Abstract

Zusammenfassung Aufgabe der amtlichen Unternehmensstatistiken ist die Bereitstellung von Informationen über Struktur und Entwicklung der Wirtschaft, die sie durch Erhebungen, die Nutzung von Verwaltungsdaten, den Zukauf kommerzieller Daten und die Verknüpfung von Mikrodaten gewinnt. In jüngster Zeit wurde darüber hinaus auch der Einsatz von Machine-Learning-Verfahren in amtlichen Unternehmensstatistiken experimentell erprobt, und zwar bei Zuordnungsentscheidungen und der Generierung neuer Informationen. In diesem Beitrag wird das Vorgehen im Überblick dargestellt. Dazu werden zunächst die Methodik des maschinellen Lernens in den Grundzügen dargestellt, bisherige Anwendungsgebiete außerhalb und in der amtlichen Statistik beschrieben sowie die in der Unternehmensstatistik experimentell eingesetzten Verfahren erläutert. Anschließend wird die praktische Anwendung von Support Vector Machines und Random Forests auf fünf konkrete Aufgabenstellungen in ausgewählten Unternehmensstatistiken dargestellt. Abschließend werden die bisherigen Erfahrungen zusammenfassend bewertet und potenzielle weitere Aufgabenstellungen sowie absehbare Weiterentwicklungen der maschinellen Lernverfahren aufgezeigt.

Suggested Citation

  • Florian Dumpert & Martin Beck, 2017. "Einsatz von Machine-Learning-Verfahren in amtlichen Unternehmensstatistiken [Use of machine learning in official business statistics]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(2), pages 83-106, October.
    • Handle: RePEc:spr:astaws:v:11:y:2017:i:2:d:10.1007_s11943-017-0208-6
    DOI: 10.1007/s11943-017-0208-6
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    References listed on IDEAS

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    1. Christina Boll & Julian Leppin, 2015. "Die geschlechtsspezifische Lohnlücke in Deutschland: Umfang, Ursachen und Interpretation," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 95(4), pages 249-254, April.
    2. Ralf Himmelreicher & Philipp vom Berge & Bernd Fitzenberger & Roland Günther & Dana Müller, 2017. "Überlegungen zur Verknüpfung von Daten der Integrierten Erwerbsbiographien (IEB) und der Verdienststrukturerhebung (VSE)," RatSWD Working Papers 262, German Data Forum (RatSWD).
    3. Gong, Joonho & Kim, Hyunjoong, 2017. "RHSBoost: Improving classification performance in imbalance data," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 1-13.
    4. Lean Yu & Shouyang Wang & Kin Keung Lai & Ligang Zhou, 2008. "Bio-Inspired Credit Risk Analysis," Springer Books, Springer, number 978-3-540-77803-5, June.
    5. Gründler, Klaus & Krieger, Tommy, 2015. "Using support vector machines for measuring democracy," Discussion Paper Series 130, Julius Maximilian University of Würzburg, Chair of Economic Order and Social Policy.
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    Cited by:

    1. Florian Dumpert & Martin Beck, 2023. "Verbesserung der Datengrundlage der Mindestlohnforschung mittels maschineller Lernverfahren [Improvement of the data basis of minimum wage research by means of machine learning methods]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 17(1), pages 5-34, March.
    2. Timo Schmid & Markus Zwick, 2017. "Editorial," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(2), pages 61-64, October.

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