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

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In this paper, we develop algorithms to independently draw from a family of conjugate posterior distributions over the structural parameterization when sign and zero restrictions are used to identify SVARs. We call this family of conjugate posterior distributions normal-generalized-normal. Our algorithms draw from a conjugate uniform-normal-inverse-Wishart posterior over the orthogonal reduced-form parameterization and transform the draws into the structural parameterization; this transformation induces a normal-generalized-normal posterior distribution over the structural parameterization. The uniform-normal-inverse-Wishart posterior over the orthogonal reduced-form parameterization has been prominent after the work of Uhlig (2005). We use Beaudry, Nam, and Wang's (2011) work on the relevance of optimism shocks to show the dangers of using alternative approaches to implement sign and zero restrictions to identify SVARs like the penalty function approach. In particular, we analytically show that the penalty function approach adds restrictions to the ones described in the identification scheme.

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File URL: https://www.frbatlanta.org/-/media/documents/research/publications/wp/2014/01b-inference-based-on-svars-identified-with-sign-and-zero-restrictions-theory-and-applications-2017-10-12.pdf
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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 Oct 2017
Handle: RePEc:fip:fedawp:2014-01
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  1. Leonardo Gambacorta & Boris Hofmann & Gert Peersman, 2014. "The Effectiveness of Unconventional Monetary Policy at the Zero Lower Bound: A Cross‐Country Analysis," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(4), pages 615-642, 06.
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  3. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
  4. Christopher A. Sims, 1986. "Are forecasting models usable for policy analysis?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-16.
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  11. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
  12. Andrew Binning, 2013. "Underidentified SVAR models: A framework for combining short and long-run restrictions with sign-restrictions," Working Paper 2013/14, Norges Bank.
  13. Waggoner, Daniel F. & Zha, Tao, 2003. "A Gibbs sampler for structural vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 28(2), pages 349-366, November.
  14. Hyungsik Roger Moon & Frank Schorfheide & Eleonora Granziera & Mihye Lee, 2011. "Inference for VARs Identified with Sign Restrictions," NBER Working Papers 17140, National Bureau of Economic Research, Inc.
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  25. repec:oup:restud:v:84:y:2017:i:3:p:1015-1040. is not listed on IDEAS
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