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dc.contributor.authorKASCHA, Christian
dc.contributor.authorMERTENS, Karel
dc.date.accessioned2007-01-12T09:28:46Z
dc.date.available2007-01-12T09:28:46Z
dc.date.issued2006
dc.identifier.issn1725-6704
dc.identifier.urihttp://hdl.handle.net/1814/6450
dc.description.abstractAn important question in empirical macroeconomics is whether structural vector autoregressions (SVARs) can reliably discriminate between competing DSGE models. Several recent papers have sug- gested that one reason SVARs may fail to do so is because they are finite-order approximations to infinite-order processes. In this context, we investigate the performance of models that do not suffer from this type of misspecification. We estimate VARMA and state space models using simulated data from a standard economic model and compare true with estimated impulse responses. For our examples, we find that one cannot gain much by using algorithms based on a VARMA rep- resentation. However, algorithms that are based on the state space representation do outperform VARs. Unfortunately, these alternative estimates remain heavily biased and very imprecise. The findings of this paper suggest that the reason SVARs perform weakly in these types of simulation studies is not because they are simple finite-order approximations. Given the properties of the generated data, their fail- ure seems almost entirely due to the use of small samples.en
dc.format.extent451512 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherEuropean University Institute
dc.relation.ispartofseriesEUI ECOen
dc.relation.ispartofseries2006/37en
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectE32en
dc.subjectC15en
dc.subjectC52en
dc.subjectStructural VARsen
dc.subjectVARMAen
dc.subjectState Space Modelsen
dc.subjectIdentificationen
dc.subjectBusiness Cyclesen
dc.titleBusiness Cycle Analysis and VARMA Modelsen
dc.typeWorking Paperen
dc.neeo.contributorKASCHA|Christian|aut|
dc.neeo.contributorMERTENS|Karel|aut|
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