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dc.contributor.authorKoop, Gary
dc.contributor.authorKorobilis, Dimitris
dc.description.abstractIn this paper we develop methods for estimation and forecasting in large timevarying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.en_US
dc.publisherUniversity of Strathclydeen_US
dc.publisherUniversity of Glasgowen_US
dc.relation.ispartofseriesSIRE DISCUSSION PAPER;SIRE-DP-2012-14
dc.subjectBayesian VARen_US
dc.subjecttime-varying coefficientsen_US
dc.subjectstate-space modelen_US
dc.titleLarge Time-Varying Parameter VARsen_US
dc.typeWorking Paperen_US

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