Large Time-Varying Parameter VARs

Gary Koop and Dimitris Korobilis

No 2012-14, SIRE Discussion Papers from Scottish Institute for Research in Economics (SIRE)

Abstract: In 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.

Keywords: Bayesian VAR; forecasting; time-varying coefficients; state-space model (search for similar items in EconPapers)
Date: 2012
New Economics Papers: this item is included in nep-for
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Citations: View citations in EconPapers (18)

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Related works:
Journal Article: Large time-varying parameter VARs (2013)
Working Paper: Large time-varying parameter VARs (2012)
Working Paper: Large time-varying parameter VARs (2012)
Working Paper: Large Time-Varying Parameter VARs (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:edn:sirdps:317

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