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dc.contributor.authorHolzmann, Hajoen
dc.contributor.authorMin, Alekseyen
dc.contributor.authorCzado, Claudiaen
dc.date.accessioned2006-09-07-
dc.date.accessioned2010-05-14T10:10:02Z-
dc.date.available2010-05-14T10:10:02Z-
dc.date.issued2006-
dc.identifier.pidoi:10.5282/ubm/epub.1846en
dc.identifier.piurn:nbn:de:bvb:19-epub-1846-3en
dc.identifier.urihttp://hdl.handle.net/10419/31063-
dc.description.abstractA new method for testing linear restrictions in linear regression models is suggested. It allows to validate the linear restriction, up to a specified approximation error and with a specified error probability. The test relies on asymptotic normality of the test statistic, and therefore normality of the errors in the regression model is not required. In a simulation study the performance of the suggested method for model selection purposes, as compared to standard model selection criteria and the t-test, is examined. As an illustration we analyze the US college spending data from 1994.en
dc.language.isoengen
dc.publisher|aLudwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen |cMünchenen
dc.relation.ispartofseries|aDiscussion Paper |x478en
dc.subject.ddc519en
dc.subject.keywordasymptotic normalityen
dc.subject.keywordlinear regressionen
dc.subject.keywordmodel selectionen
dc.subject.keywordmodel validationen
dc.titleValidating linear restrictions in linear regression models with general error structure-
dc.type|aWorking Paperen
dc.identifier.ppn51717104Xen
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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