Bayesian Structural VAR models: a new approach for prior beliefs on impulse responses
Martin Bruns () and
No 878, Working Papers from Queen Mary University of London, School of Economics and Finance
Structural VAR models are frequently identified using sign restrictions on impulse responses. Moving beyond the popular but restrictive Normal-inverse-Wishart-Uniform prior, we develop a methodology that can handle almost any prior distribution on contemporaneous responses. We then propose a new sampler that explores the posterior just as efficiently as done by the existing algorithm for the Normal-inverse-Wishart-Uniform case. We use this exible and tractable framework to combine sign restrictions with information on the volatility of the data, giving less prior mass to impulse effects that are inconsistent with the data from a training sample. This approach sharpens posterior bands and makes sign restrictions more informative. We apply the methodology to the oil market and show that oil supply shocks have a non-negligible effect on oil price dynamics.
Keywords: Sign restrictions; Bayesian inference; Oil market (search for similar items in EconPapers)
JEL-codes: C32 C11 E50 H62 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed
Downloads: (external link)
Working Paper: Bayesian Structural VAR Models: A New Approach for Prior Beliefs on Impulse Responses (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:qmw:qmwecw:878
Access Statistics for this paper
More papers in Working Papers from Queen Mary University of London, School of Economics and Finance Contact information at EDIRC.
Bibliographic data for series maintained by Nicholas Owen ( this e-mail address is bad, please contact ).