Data-based priors for vector autoregressions with drifting coefficients

Dimitris Korobilis

Working Papers from Business School - Economics, University of Glasgow

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)
JEL-codes: C11 C22 C32 C52 C53 C63 E17 E58 (search for similar items in EconPapers)
Date: 2014-01
New Economics Papers: this item is included in nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Related works:
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:gla:glaewp:2014_04

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