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Global identification of linearized DSGE models

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dc.abstract.enThis paper introduces a computational framework to analyze global identification of linearized DSGE models. A formal identification condition is established that relies on the restrictions linking the observationally equivalent state space representations and on the inherent constraints imposed by the model solution on the deep parameters. This condition is next used to develop an algorithm that checks global identification by searching for observationally equivalent model parametrizations. The algorithm is efficient as the identification conditions it employs shrink considerably the space of candidate deep parameter points and the model does not need to be solved at each of these points. The working of the algorithm is demonstrated with two examples.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorKolasa, Marcin
dc.contributor.authorKocięcki, Andrzej
dc.date.accessioned2024-01-25T02:07:03Z
dc.date.available2024-01-25T02:07:03Z
dc.date.copyright2018-11-28
dc.date.issued2018
dc.description.accesstimeAT_PUBLICATION
dc.description.financeNie dotyczy
dc.description.number3
dc.description.versionFINAL_PUBLISHED
dc.description.volume9
dc.identifier.issn1759-7323
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/107964
dc.identifier.weblinkhttps://onlinelibrary.wiley.com/doi/full/10.3982/qe530
dc.languageeng
dc.pbn.affiliationeconomics and finance
dc.relation.ispartofQuantitative Economics
dc.relation.pages1243-1263
dc.rightsCC-BY-NC
dc.sciencecloudnosend
dc.subject.enGlobal identification, DSGE models, state-space representation
dc.titleGlobal identification of linearized DSGE models
dc.typeReviewArticle
dspace.entity.typePublication