Bayesian Structural VAR Models: A New Approach for Prior Beliefs on Impulse Responses

37 Pages Posted: 6 Apr 2019

See all articles by Martin Bruns

Martin Bruns

University of East Anglia (UEA)

Michele Piffer

Queen Mary, University of London

Date Written: March 2019

Abstract

Fairtrade certification aims at transferring wealth from the consumer to the farmer; however, coffee passes through many hands before reaching final consumers. Bringing together retail, wholesale, and stock market data, this study estimates how much more consumers are paying for Structural VAR models are frequently identified using sign restrictions on contemporaneous impulse responses. We develop a methodology that can handle a set of prior distributions that is much larger than the one currently allowed for by traditional methods. We then develop an importance sampler that explores the posterior distribution just as conveniently as with traditional approaches. This makes the existing trade-off between careful prior selection and tractable posterior sampling disappear. We use this framework to combine sign restrictions with information on the volatility of the variables in the model, and show that this sharpens posterior inference. Applying the methodology to the oil market, we find that supply shocks have a strong role in driving the dynamics of the price of oil and in explaining the drop in oil production during the Gulf war.

Keywords: Sign restrictions, Bayesian inference, Oil market

JEL Classification: C32, C11, E50, H62

Suggested Citation

Bruns, Martin and Piffer, Michele, Bayesian Structural VAR Models: A New Approach for Prior Beliefs on Impulse Responses (March 2019). DIW Berlin Discussion Paper No. 1796 (2019). Available at SSRN: https://ssrn.com/abstract=3366913 or http://dx.doi.org/10.2139/ssrn.3366913

Martin Bruns (Contact Author)

University of East Anglia (UEA) ( email )

Norwich Research Park
Norwich, Norfolk NR4 7TJ
United Kingdom

Michele Piffer

Queen Mary, University of London ( email )

Mile End Road
London, E1 4NS
Great Britain
02078828712 (Phone)

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