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dc.contributor.authorPetrova, Katerina
dc.date.accessioned2021-04-12T23:47:15Z
dc.date.available2021-04-12T23:47:15Z
dc.date.issued2019-09
dc.identifier.citationPetrova , K 2019 , ' A quasi-Bayesian local likelihood approach to time varying parameter VAR models ' , Journal of Econometrics , vol. 212 , no. 1 , pp. 286-306 . https://doi.org/10.1016/j.jeconom.2019.04.031en
dc.identifier.issn0304-4076
dc.identifier.otherPURE: 252974539
dc.identifier.otherPURE UUID: 1b47ec41-f352-4631-ba76-b2e1140dbed8
dc.identifier.otherRIS: urn:DF5B4AB6EF3A744C5C9E90AF84E32872
dc.identifier.otherRIS: urn:DF5B4AB6EF3A744C5C9E90AF84E32872
dc.identifier.otherORCID: /0000-0002-3155-2938/work/57088526
dc.identifier.otherScopus: 85064614642
dc.identifier.otherWOS: 000484874800015
dc.identifier.urihttp://hdl.handle.net/10023/23017
dc.description.abstractThe paper establishes a quasi-Bayesian local likelihood (QBLL) estimation methodology for a multivariate model with time varying parameters. The asymptotic validity of the resulting quasi-posterior distributions of the drifting parameters is proven in general and, in the special case of a Gaussian VAR model, a closed form time varying Normal-Wishart expression for the quasi-posterior distribution of the parameters is provided. In addition, this paper develops several Gibbs algorithms, which can sample from a VAR model with a mixture of time varying and time invariant parameters. The proposed estimators differ from existing state space approaches to VAR models in that they estimate parameter time variation nonparametrically, without imposing parametric stochastic processes on the parameters. The QBLL estimators are robust to misspecification of the state equation and exhibit good finite sample performance, even when compared to correctly specified parametric state space models, as illustrated by a Monte Carlo exercise. In addition, we demonstrate that the QBLL approach provides a remedy to the ‘curse of dimensionality’ by accommodating large dimensional VAR systems and delivers improvements in the out-of-sample forecasts of key macroeconomic variables. Finally, the paper makes an empirical contribution to the literature on changing inflation dynamics in the U.S., presenting evidence of a fall in inflation persistence and volatility during the Great Moderation period.
dc.language.isoeng
dc.relation.ispartofJournal of Econometricsen
dc.rightsCrown Copyright © 2019 Published by Elsevier B.V. All rights reserved. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1016/j.jeconom.2019.04.031en
dc.subjectTime varying parametersen
dc.subjectChanging volatilityen
dc.subjectMonetary policyen
dc.subjectBayesian methodsen
dc.subjectHB Economic Theoryen
dc.subjectHG Financeen
dc.subjectT-NDASen
dc.subjectBDCen
dc.subjectR2Cen
dc.subject.lccHBen
dc.subject.lccHGen
dc.titleA quasi-Bayesian local likelihood approach to time varying parameter VAR modelsen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.description.versionOtheren
dc.contributor.institutionUniversity of St Andrews.School of Economics and Financeen
dc.identifier.doihttps://doi.org/10.1016/j.jeconom.2019.04.031
dc.description.statusPeer revieweden
dc.date.embargoedUntil2021-04-13


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