Block-recursive non-Gaussian structural vector autoregressions

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dc.contributor.authorKeweloh, Sascha Alexander
dc.contributor.authorHetzenecker, Stephan
dc.contributor.authorSeepe, Andre
dc.date.accessioned2021-11-09T15:05:40Z
dc.date.available2021-11-09T15:05:40Z
dc.date.issued2021
dc.description.abstractThis study combines block-recursive restrictions with higher-order moment conditions to identify and estimate non-Gaussian structural vector autoregressions. The estimator allows to impose a block-recursive structure on the SVAR and for a given block-recursive structure we derive a conservative set of assumptions on the dependence and Gaussianity of the shocks to ensure identification. We use a Monte Carlo simulation to illustrate the advantages of the proposed blockrecursive estimator compared to unrestricted, purely data driven estimators in small samples. The block-recursive estimator is used to analyze the interdependence of monetary policy and the stock market. We find that a positive stock market shock contemporaneously increases the nominal interest rate, while contractionary monetary policy shocks lead to lower stock returns on impact.en
dc.identifier.urihttp://hdl.handle.net/2003/40548
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-22417
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;23/2021
dc.subjectSVARen
dc.subjectmonetary policyen
dc.subjectstock marketen
dc.subjectblock-recursiveen
dc.subjectnon-Gaussianityen
dc.subjectidentificationen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleBlock-recursive non-Gaussian structural vector autoregressionsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

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Sonderforschungsbereich (SFB) 823