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Structural vector autoregressions and information in moments beyond the variance

dc.contributor.advisorLinnemann, Ludger
dc.contributor.authorKeweloh, Sascha Alexander
dc.contributor.refereeJentsch, Carsten
dc.date.accepted2022-08-02
dc.date.accessioned2022-09-08T05:32:37Z
dc.date.available2022-09-08T05:32:37Z
dc.date.issued2022
dc.description.abstractThis dissertation is concerned with the estimation of the simultaneous interaction in non- Gaussian SVAR models using generalized method of moments (GMM) estimators with higher- order moment conditions. The dissertation contributes to the literature by providing identification results using higher-order moment conditions derived from the assumption of independent structural shocks, by proposing modifications to the GMM estimation procedure to improve the small sample performance in the presence of higher-order moment conditions, and by developing a framework to combine traditional restriction based approaches with data-driven identification and estimation approaches.de
dc.identifier.urihttp://hdl.handle.net/2003/41066
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-22913
dc.language.isoende
dc.subjectStructural vector autoregressionde
dc.subjectIdentificationde
dc.subjectNon-Gaussiande
dc.subjectIndependentde
dc.subjectGMMde
dc.subject.ddc330
dc.subject.rswkVektor-autoregressives Modellde
dc.subject.rswkNichtgaußscher Prozessde
dc.titleStructural vector autoregressions and information in moments beyond the variancede
dc.typeTextde
dc.type.publicationtypedoctoralThesisde
dcterms.accessRightsopen access
eldorado.dnb.deposittruede
eldorado.secondarypublicationfalsede

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