Sign restrictions in high-dimensional vector autoregressions

Dimitris Korobilis

Working Papers from Business School - Economics, University of Glasgow

Abstract: This paper proposes a new Bayesian sampling scheme for inference in vector autoregressions (VARs) using sign restrictions. I build on a factor model decomposition of the reduced-form VAR disturbances, which are assumed to be driven by a few common factors/shocks. The outcome is a computationally efficient algorithm that allows to jointly sample VAR parameters as well as decompositions of the covariance matrix satisfying desired sign restrictions. Using artificial and real data I show that the new algorithm works well and is multiple times more efficient than existing accept/reject algorithms for sign restrictions.

Keywords: high-dimensional VAR; structural inference; factor model; sign restriction; Gibbs sampling (search for similar items in EconPapers)
JEL-codes: C11 C13 C15 C22 C52 C53 C61 (search for similar items in EconPapers)
Date: 2020-09
New Economics Papers: this item is included in nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Working Paper: Sign restrictions in high-dimensional vector autoregressions (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:gla:glaewp:2020_21

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