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Estimating overidentified, nonrecursive, time-varying coefficients structural VARs

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  • Fabio Canova

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  • Fernando J. Pérez Forero

Abstract

This paper provides a method to estimate time varying coefficients structural VARs which are non-recursive and potentially overidentified. The procedure allows for linear and non-linear restrictions on the parameters, maintains the multi-move structure of standard algorithms and can be used to estimate structural models with different identification restrictions. We study the transmission of monetary policy shocks and compare the results with those obtained with traditional methods.

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Bibliographic Info

Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 1321.

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Date of creation: May 2012
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Handle: RePEc:upf:upfgen:1321

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Web page: http://www.econ.upf.edu/

Related research

Keywords: Non-recursive overidentified SVARs; Time-varying coefficient models; Bayesian methods; Monetary transmission mechanism;

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  1. Fabio Canova & Filippo Ferroni, 2010. "The Dynamics of US Inflation: Can Monetary Policy Explain the Changes?," Working Papers 471, Barcelona Graduate School of Economics.
  2. Ben S. Bernanke & Alan S. Blinder, 1989. "The federal funds rate and the channels of monetary transmission," Working Papers 89-10, Federal Reserve Bank of Philadelphia.
  3. David B. Gordon & Eric M. Leeper, 1993. "The dynamic impacts of monetary policy: an exercise in tentative identification," Working Paper, Federal Reserve Bank of Atlanta 93-5, Federal Reserve Bank of Atlanta.
  4. Giorgio Primiceri & Alejandro Justiniano, 2006. "The Time Varying Volatility of Macroeconomic Fluctuations," 2006 Meeting Papers, Society for Economic Dynamics 353, Society for Economic Dynamics.
  5. Kim, Sangjoon & Shephard, Neil & Chib, Siddhartha, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 65(3), pages 361-93, July.
  6. Luca Gambetti & Evi Pappa & Fabio Canova, 2008. "The Structural Dynamics of U.S. Output and Inflation: What Explains the Changes?," Journal of Money, Credit and Banking, Blackwell Publishing, Blackwell Publishing, vol. 40(2-3), pages 369-388, 03.
  7. Bernanke, Ben S. & Mihov, Ilian, 1995. "Measuring Monetary Policy," Economics Series, Institute for Advanced Studies 10, Institute for Advanced Studies.
  8. Juan F. Rubio-Ramírez & Daniel F.Waggoner & Tao Zha, 2008. "Structural vector autoregressions: theory of identification and algorithms for inference," Working Paper, Federal Reserve Bank of Atlanta 2008-18, Federal Reserve Bank of Atlanta.
  9. Jean Boivin & Marc P. Giannoni, 2003. "Has Monetary Policy Become More Effective?," NBER Working Papers 9459, National Bureau of Economic Research, Inc.
  10. Canova, Fabio & Ciccarelli, Matteo & Ortega, Eva, 2012. "Do institutional changes affect business cycles? Evidence from Europe," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 36(10), pages 1520-1533.
  11. Canova, Fabio & Paustian, Matthias, 2011. "Business cycle measurement with some theory," CEPR Discussion Papers, C.E.P.R. Discussion Papers 8364, C.E.P.R. Discussion Papers.
  12. Eric M. Leeper & Christopher A. Sims & Tao Zha, 1996. "What Does Monetary Policy Do?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 27(2), pages 1-78.
  13. Canova, Fabio & Gambetti, Luca, 2009. "Structural changes in the US economy: Is there a role for monetary policy?," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 33(2), pages 477-490, February.
  14. Christopher A. Sims & Tao Zha, 1996. "Bayesian methods for dynamic multivariate models," Working Paper, Federal Reserve Bank of Atlanta 96-13, Federal Reserve Bank of Atlanta.
  15. Markku Lanne, Helmut Luetkepohl, 2006. "Identifying Monetary Policy Shocks via Changes in Volatility," Economics Working Papers, European University Institute ECO2006/23, European University Institute.
  16. Christopher A. Sims & Tao Zha, 2004. "Were there regime switches in U.S. monetary policy?," Working Paper, Federal Reserve Bank of Atlanta 2004-14, Federal Reserve Bank of Atlanta.
  17. Geweke, John & Tanizaki, Hisashi, 2001. "Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 37(2), pages 151-170, August.
  18. Robertson, John C & Tallman, Ellis W, 2001. "Improving Federal-Funds Rate Forecasts in VAR Models Used for Policy Analysis," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 19(3), pages 324-30, July.
  19. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
  20. Waggoner, Daniel F. & Zha, Tao, 2003. "A Gibbs sampler for structural vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 28(2), pages 349-366, November.
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Cited by:
  1. Canova, Fabio & Ciccarelli, Matteo, 2013. "Panel Vector Autoregressive Models: A Survey," CEPR Discussion Papers, C.E.P.R. Discussion Papers 9380, C.E.P.R. Discussion Papers.

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