Fast Posterior Sampling in Tightly Identified SVARs Using 'Soft' Sign Restrictions

Matthew Read and Dan Zhu
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Dan Zhu: Department of Econometrics and Business Statistics, Monash University

RBA Research Discussion Papers from Reserve Bank of Australia

Abstract: We propose algorithms for conducting Bayesian inference in structural vector autoregressions identified using sign restrictions. The key feature of our approach is a sampling step based on 'soft' sign restrictions. This step draws from a target density that smoothly penalises parameter values violating the restrictions, facilitating the use of computationally efficient Markov chain Monte Carlo sampling algorithms. An importance-sampling step yields draws from the desired distribution conditional on the 'hard' sign restrictions. Relative to standard accept-reject sampling, the method substantially improves computational efficiency when identification is 'tight'. It can also greatly reduce the computational burden of implementing prior-robust Bayesian methods. We illustrate the broad applicability of the approach in a model of the global oil market identified using a rich set of sign, elasticity and narrative restrictions.

Keywords: Bayesian inference; Markov chain Monte Carlo; oil market; sign restrictions; structural vector autoregression (search for similar items in EconPapers)
JEL-codes: C32 Q35 Q43 (search for similar items in EconPapers)
Date: 2025-05
New Economics Papers: this item is included in nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:rba:rbardp:rdp2025-03

DOI: 10.47688/rdp2025-03

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