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dc.contributor.authorKASCHA, Christian
dc.date.accessioned2007-06-29T15:17:35Z
dc.date.available2007-06-29T15:17:35Z
dc.date.issued2007
dc.identifier.issn1725-6704
dc.identifier.urihttp://hdl.handle.net/1814/6921
dc.description.abstractClassical Gaussian maximum likelihood estimation of mixed vector autoregressive moving-average models is plagued with various numerical problems and has been considered difficult by many applied researchers. These disadvantages could have led to the dominant use of vector autoregressive models in macroeconomic research. Therefore, several other, simpler estimation methods have been proposed in the literature. In this paper these methods are compared by means of a Monte Carlo study. Different evaluation criteria are used to judge the relative performances of the algorithms.en
dc.format.extent899626 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherEuropean University Institute
dc.relation.ispartofseriesEUI ECOen
dc.relation.ispartofseries2007/12en
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectC32en
dc.subjectC15en
dc.subjectC63en
dc.subjectVARMA Modelsen
dc.subjectEstimation Algorithmsen
dc.subjectForecastingen
dc.titleA Comparison of Estimation Methods for Vector Autoregressive Moving-Average Modelsen
dc.typeWorking Paperen
dc.neeo.contributorKASCHA|Christian|aut|
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