Clustering techniques for the detection of Business Cycles

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DC Field	Value	Language
dc.contributor.author	Theis, W.	de
dc.contributor.author	Weihs, C.	de
dc.date.accessioned	2004-12-06T18:39:49Z	-
dc.date.available	2004-12-06T18:39:49Z	-
dc.date.issued	1999	de
dc.identifier.uri	http://hdl.handle.net/2003/4927	-
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15027	-
dc.description.abstract	In this paper business cycles are considered as a multivariate phenomenon and not as a univariate one determined e.g. by the GNP. The subject is to look for the number of phases of a business cycle, which can be motivated by the number of clusters in a given dataset of macro-economic variables. Different approaches to distances in the data are tried in a fuzzy cluster analysis to pursue this goal.	en
dc.format.extent	202234 bytes	-
dc.format.extent	897846 bytes	-
dc.format.mimetype	application/pdf	-
dc.format.mimetype	application/postscript	-
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	business cycles	en
dc.subject	cluster analysis	en
dc.subject	fuzzy clustering	en
dc.subject.ddc	310	de
dc.title	Clustering techniques for the detection of Business Cycles	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access	-
Appears in Collections:	Sonderforschungsbereich (SFB) 475

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