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dc.contributor.authorCanova, Fabio
dc.contributor.authorMatthes, Christian
dc.date.accessioned2023-01-20T13:10:59Z
dc.date.available2023-01-20T13:10:59Z
dc.date.created2021-06-08T13:30:43Z
dc.date.issued2021
dc.identifier.citationQuantitative Economics. 2021, 12 (2), 313-350.en_US
dc.identifier.issn1759-7323
dc.identifier.urihttps://hdl.handle.net/11250/3044971
dc.description.abstractWe consider a set of potentially misspecified structural models, geometrically combine their likelihood functions, and estimate the parameters using composite methods. In a Monte Carlo study, composite estimators dominate likelihood‐based estimators in mean squared error and composite models are superior to individual models in the Kullback–Leibler sense. We describe Bayesian quasi‐posterior computations and compare our approach to Bayesian model averaging, finite mixture, and robust control procedures. We robustify inference using the composite posterior distribution of the parameters and the pool of models. We provide estimates of the marginal propensity to consume and evaluate the role of technology shocks for output fluctuations.en_US
dc.language.isoengen_US
dc.publisherThe Econometric Societyen_US
dc.rightsNavngivelse-Ikkekommersiell 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/deed.no*
dc.titleDealing with misspecification in structural macroeconometric modelsen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderThe Authorsen_US
dc.source.pagenumber313-350en_US
dc.source.volume12en_US
dc.source.journalQuantitative Economicsen_US
dc.source.issue2en_US
dc.identifier.doi10.3982/QE1413
dc.identifier.cristin1914534
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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Navngivelse-Ikkekommersiell 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse-Ikkekommersiell 4.0 Internasjonal