Sign restrictions in high-dimensional vector autoregressions

Dimitris Korobilis

Working Paper series from Rimini Centre for Economic Analysis

Abstract: This paper proposes a new Bayesian sampling scheme for VAR inference using sign restrictions. We build on a factor model decomposition of the reduced-form VAR disturbances, which are assumed to be driven by a few fundamental 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 we show that the new algorithm works well and is multiple times more efficient than existing accept/reject algorithms for sign restrictions.

Keywords: high-dimensional inference; Structural VAR; Markov chain Monte Carlo; set identification (search for similar items in EconPapers)
JEL-codes: C11 C13 C15 C22 C52 C53 C61 (search for similar items in EconPapers)
Date: 2020-03
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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http://rcea.org/RePEc/pdf/wp20-09.pdf

Related works:
Working Paper: Sign restrictions in high-dimensional vector autoregressions (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:20-09

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