Data-Based Priors for Vector Autoregressions with Drifting Coefficients

25 Pages Posted: 8 Feb 2014

See all articles by Dimitris Korobilis

Dimitris Korobilis

University of Glasgow - Adam Smith Business School

Date Written: February 6, 2014

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

JEL Classification: C11, C22, C32, C52, C53, C63, E17, E58

Suggested Citation

Korobilis, Dimitris, Data-Based Priors for Vector Autoregressions with Drifting Coefficients (February 6, 2014). Available at SSRN: https://ssrn.com/abstract=2392028 or http://dx.doi.org/10.2139/ssrn.2392028

Dimitris Korobilis (Contact Author)

University of Glasgow - Adam Smith Business School ( email )

40 University Avenue
Gilbert Scott Building
Glasgow, Scotland G12 8QQ
United Kingdom

HOME PAGE: http://https://sites.google.com/site/dimitriskorobilis/

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