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Structural VARs, deterministic and stochastic trends: how much detrending matters for shock identification

Wiriyawit, Varang; Wong, Benjamin

Description

Detrending within structural vector autoregressions (SVAR) is directly linked to the shock identification. We investigate the consequences of trend misspecification in an SVAR using both standard real business cycle models and bi-variate SVARs as data generating processes. Our bias decomposition reveals biases arising directly from trend misspecification are not trivial when compared to other widely studied misspecifications. Misspecifying the trend also distorts impulse response functions of...[Show more]

dc.contributor.authorWiriyawit, Varang
dc.contributor.authorWong, Benjamin
dc.date.accessioned2016-11-01T00:24:55Z
dc.date.available2016-11-01T00:24:55Z
dc.identifier.issn1558-3708
dc.identifier.urihttp://hdl.handle.net/1885/109787
dc.description.abstractDetrending within structural vector autoregressions (SVAR) is directly linked to the shock identification. We investigate the consequences of trend misspecification in an SVAR using both standard real business cycle models and bi-variate SVARs as data generating processes. Our bias decomposition reveals biases arising directly from trend misspecification are not trivial when compared to other widely studied misspecifications. Misspecifying the trend also distorts impulse response functions of even the correctly detrended variable within the SVAR system. Pretesting for unit roots mitigates trend misspecification to some extent. We also find that while practitioners can specify high lag order VARs to mitigate trend misspecification, relying on common information criterion such as the Akaike information criterion (AIC) or Bayesian information criterion (BIC) may choose a lag order that is too low.
dc.publisherDe Gruyter
dc.rights© 2016 by De Gruyter.
dc.sourceStudies in Nonlinear Dynamics & Econometrics
dc.subjectbias
dc.subjectdetrending
dc.subjectidentification
dc.subjectstructural VAR
dc.titleStructural VARs, deterministic and stochastic trends: how much detrending matters for shock identification
dc.typeJournal article
local.identifier.citationvolume20
dc.date.issued2015-10
local.publisher.urlhttp://www.degruyter.com/
local.type.statusPublished Version
local.contributor.affiliationWong, B., Centre for Applied Macroeconomic Analysis, The Australian National University
local.bibliographicCitation.issue2
local.bibliographicCitation.startpage141
local.bibliographicCitation.lastpage157
local.identifier.doi10.1515/snde-2015-0030
CollectionsANU Research Publications

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