Efficiency gains in structural vector autoregressions by selecting informative higher-order moment conditions

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dc.contributor.authorKeweloh, Sascha Alexander
dc.contributor.authorHetzenecker, Stephan
dc.date.accessioned2021-11-28T16:11:05Z
dc.date.available2021-11-28T16:11:05Z
dc.date.issued2021
dc.description.abstractThis study combines block-recursive restrictions with non-Gaussian and mean independent shocks to derive identifying and overidentifying higher-order moment conditions for structural vector autoregressions. We show that overidentifying higher-order moments can contain additional information and increase the efficiency of the estimation. In particular, we prove that in the non-Gaussian recursive SVAR higher-order moment conditions are relevant and therefore, the frequently applied estimator based on the Cholesky decomposition is inefficient. Even though incorporating information in valid higher-order moments is asymptotically efficient, including many redundant and potentially even invalid moment conditions renders standard SVAR GMM estimators unreliable in finite samples. We apply a LASSO-type GMM estimator to select the relevant and valid higher-order moment conditions, increasing finite sample precision. A Monte Carlo experiment and an application to quarterly U.S. data illustrate the improved performance of the proposed estimator.en
dc.identifier.urihttp://hdl.handle.net/2003/40578
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-22447
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;26/2021
dc.subjectSVARen
dc.subjectmonetary policyen
dc.subjectLASSOen
dc.subjectblock-recursiveen
dc.subjectnon-Gaussianityen
dc.subjectefficiencyen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleEfficiency gains in structural vector autoregressions by selecting informative higher-order moment conditionsde
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

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