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The Systematic Component of Monetary Policy in SVARs: An Agnostic Identification Procedure

Listed author(s):
  • Jonas E. Arias
  • Dario Caldara
  • Juan F. Rubio-Ramírez

In this paper, we identify monetary policy shocks in structural vector autoregressions (SVARs) by imposing sign and zero restrictions on the systematic component of monetary policy while leaving the remaining equations in the system unrestricted. As in Uhlig (2005), no restrictions are imposed on the response of output to a monetary policy shock. We find that an exogenous increase in the federal funds rate leads to a persistent decline in output and prices. Our results show that the contractionary effects of monetary policy shocks do not hinge on questionable exclusion restrictions, but are instead consistent with agnostic identification schemes. The analysis is robust to various specifications of the systematic component of monetary policy widely used in the literature.

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File URL: http://documentos.fedea.net/pubs/dt/2014/dt-2014-13.pdf
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Paper provided by FEDEA in its series Working Papers with number 2014-13.

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Date of creation: Oct 2014
Handle: RePEc:fda:fdaddt:2014-13
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  1. 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.
  2. Waggoner, Daniel F. & Zha, Tao, 2003. "Likelihood preserving normalization in multiple equation models," Journal of Econometrics, Elsevier, vol. 114(2), pages 329-347, June.
  3. Christiano, Lawrence J & Eichenbaum, Martin & Evans, Charles, 1996. "The Effects of Monetary Policy Shocks: Evidence from the Flow of Funds," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 16-34, February.
  4. Jonas E. Arias & Juan Rubio-Ramirez & Daniel F. Waggoner, 2013. "Inference Based on SVARs Identied with Sign and Zero Restrictions: Theory and Applications," Working Papers 2013-24, FEDEA.
  5. G. Peersman & W. Wagner, 2014. "Shocks to Bank Lending, Risk-Taking, Securitization, and their Role for U.S. Business Cycle Fluctuations," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 14/874, Ghent University, Faculty of Economics and Business Administration.
  6. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," Review of Economic Studies, Oxford University Press, vol. 77(2), pages 665-696.
  7. Julio Rotemberg & Michael Woodford, 1997. "An Optimization-Based Econometric Framework for the Evaluation of Monetary Policy," NBER Chapters, in: NBER Macroeconomics Annual 1997, Volume 12, pages 297-361 National Bureau of Economic Research, Inc.
  8. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
  9. Michael T. Belongia & Peter N. Ireland, 2015. "Interest Rates and Money in the Measurement of Monetary Policy," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 255-269, April.
  10. Andrew Binning, 2013. "Underidentified SVAR models: A framework for combining short and long-run restrictions with sign-restrictions," Working Paper 2013/14, Norges Bank.
  11. Henry W. Chappell, Jr. & Rob Roy McGregor & Todd A. Vermilyea, 2005. "Committee Decisions on Monetary Policy: Evidence from Historical Records of the Federal Open Market Committee," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262033305.
  12. Paul Beaudry & Deokwoo Nam & Jian Wang, 2011. "Do Mood Swings Drive Business Cycles and is it Rational?," NBER Working Papers 17651, National Bureau of Economic Research, Inc.
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