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Inference Based on SVARs Identified with Sign and Zero Restrictions: Theory and Applications

In this paper we characterize agnostic and conditionally agnostic priors and propose numerical algorithms for Bayesian inference when using sign and zero restrictions to identify SVARs. As Baumeister and Hamilton (2015a) have made clear, priors play a crucial role in this environment. If the prior, subject to the sign and zero restrictions, is not conditionally agnostic, then the prior affects identification. Hence, identification does not solely come from the sign and zero restrictions stated by the researcher. Our numerical algorithms show how to do inference based on SVARs using conditionally agnostic priors and posteriors subject to sign and zero restrictions. We use Beaudry, Nam and Wang's (2011) work on the relevance of optimism shocks to show the dangers of using priors that are not conditionally agnostic subject to the sign and zero restrictions.

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Paper provided by Federal Reserve Bank of Atlanta in its series FRB Atlanta Working Paper with number 2014-1.

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Length: 46 pages
Date of creation: 01 Feb 2014
Date of revision: 01 Nov 2016
Handle: RePEc:fip:fedawp:2014-01
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