Data-based priors for vector autoregressions with drifting coefficients

Dimitris Korobilis

No 2014-022, SIRE Discussion Papers from Scottish Institute for Research in Economics (SIRE)

Abstract: This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require minimal input by the user, and they result in shrinkage posterior representations, thus, making them appropriate for models of large dimensions. A comprehensive forecasting exercise involving TVP-VARs of different dimensions establishes the usefulness of the proposed approach.

Keywords: TVP-VAR; shrinkage; data-based prior; forecasting (search for similar items in EconPapers)
Date: 2014-01
New Economics Papers: this item is included in nep-ets and nep-for
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Citations: View citations in EconPapers (14)

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Working Paper: Data-based priors for vector autoregressions with drifting coefficients (2014)
Working Paper: Data-based priors for vector autoregressions with drifting coefficients (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:edn:sirdps:567

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