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Bottom-up economic forecasting of regional unemployment in Germany
Kern, Christoph
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Lin, Cinny
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Ganesh, Vighnesh Natarajan
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Rathi, Prakhar
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Sasson, Amit
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Document Type:
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Book chapter
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Year of publication:
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2022
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Book title:
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Neue Dimensionen in Data Science : Interdisziplinäre Ansätze und Anwendungen aus Wissenschaft und Wirtschaft
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Page range:
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299-311
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Publisher:
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Wawrzyniak, Barbara
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Herter, Michael
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Place of publication:
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Berlin
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Publishing house:
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Wichmann Verlag
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ISBN:
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978-3-87907-721-2 , 3-87907-721-5 , 978-3-87907-722-9
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Related URLs:
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Publication language:
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English
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Institution:
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School of Social Sciences > Social Data Science and Methodology (Keusch 2022-)
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Subject:
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330 Economics
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Abstract:
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In this chapter, we present and compare various approaches to grouping counties to optimize forecasts of regional unemployment rates. As a reference, we first group counties based on their administrative structures, i.e., each county's state. This approach is compared to a set of data-driven techniques that utilize unsupervised machine learning. We show that grouping counties based on arbitrary historic borders may not be the best approach, while clustering the counties based on their economic similarities may improve forecasting results.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Search Authors in
BASE:
Kern, Christoph
;
Lin, Cinny
;
Ganesh, Vighnesh Natarajan
;
Rathi, Prakhar
;
Sasson, Amit
Google Scholar:
Kern, Christoph
;
Lin, Cinny
;
Ganesh, Vighnesh Natarajan
;
Rathi, Prakhar
;
Sasson, Amit
ORCID:
Kern, Christoph  ORCID: https://orcid.org/0000-0001-7363-4299, Lin, Cinny, Ganesh, Vighnesh Natarajan, Rathi, Prakhar and Sasson, Amit
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