Arbeitspapier
Adaptive weights clustering of research papers
The JEL classification system is a standard way of assigning key topics to economic articles in order to make them more easily retrievable in the bulk of nowadays massive literature. Usually the JEL (Journal of Economic Literature) is picked by the author(s) bearing the risk of suboptimal assignment. Using the database of a Collaborative Research Center from Humboldt-Universität zu Berlin and Xiamen University, China we employ a new adaptive clustering technique to identify interpretable JEL (sub)clusters. The proposed Adaptive Weights Clustering (AWC) is available on www.quantlet.de and is based on the idea of locally weighting each point (document, abstract) in terms of cluster membership. Comparison with k-means or CLUTO reveals excellent performance of AWC.
- Sprache
-
Englisch
- Erschienen in
-
Series: SFB 649 Discussion Paper ; No. 2017-013
- Klassifikation
-
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Large Data Sets: Modeling and Analysis
Financial Econometrics
Portfolio Choice; Investment Decisions
Financial Forecasting and Simulation
- Thema
-
Clustering
JEL system
Adaptive algorithm
Economic articles
Nonparametric
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Adamyan, Larisa
Efimov, Kirill
Chen, Cathy Yi-hsuan
Härdle, Wolfgang Karl
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
- (wo)
-
Berlin
- (wann)
-
2017
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:22 MESZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Adamyan, Larisa
- Efimov, Kirill
- Chen, Cathy Yi-hsuan
- Härdle, Wolfgang Karl
- Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
Entstanden
- 2017
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