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dc.contributor.authorBayer, Christianen_US
dc.contributor.authorHanck, Christophen_US
dc.date.accessioned2009-05-19en_US
dc.date.accessioned2010-07-15T10:08:00Z-
dc.date.available2010-07-15T10:08:00Z-
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/10419/36613-
dc.description.abstractThis paper suggests a combination procedure to exploit the imperfect correlation of cointegration tests to develop a more powerful meta test. To exemplify, we combine Engle and Granger (1987) and Johansen (1988) tests. Either of these underlying tests can be more powerful than the other one depending on the nature of the data-generating process. The new meta test is at least as powerful as the more powerful one of the underlying tests irrespective of the very nature of the data generating process. At the same time, our new meta test avoids the size distortion inherent in separately applying multiple tests for cointegration to the same data set.en_US
dc.language.isoengen_US
dc.publisherUniv., SFB 475 Dortmunden_US
dc.relation.ispartofseriesTechnical Report // Sonderforschungsbereich 475, Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund 2008,10en_US
dc.subject.jelC12en_US
dc.subject.jelC22en_US
dc.subject.ddc310en_US
dc.subject.keywordCointegrationen_US
dc.subject.keywordMeta Testen_US
dc.subject.keywordMultiple Testingen_US
dc.titleIs double trouble? How to combine cointegration testsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn600066878en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:sfb475:200810-
Appears in Collections:Technical Reports, SFB 475: Komplexitätsreduktion in multivariaten Datenstrukturen, TU Dortmund

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