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A Statistical Comparison of Alternative Identification Schemes for Monetary Policy Shocks

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Author Info
Markku Lanne
Helmut Luetkepohl

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Abstract

Different identification schemes for monetary policy shocks have been proposed in the literature. They typically specify just-identifying restrictions in a standard structural vector autoregressive (SVAR) framework. Thus, in this framework the different schemes cannot be checked against the data with statistical tests. We consider different approaches how to use the data properties to augment the standard SVAR setup for identifying the shocks. Thereby it becomes possible to test models which are just identified in a standard setting. For monthly US data it is found that a model where monetary shocks are induced via the federal funds rate is the only one which cannot be rejected when the data properties are used for identification.

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Paper provided by European University Institute in its series Economics Working Papers with number ECO2008/23.

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Date of creation: 2008
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Handle: RePEc:eui:euiwps:eco2008/23

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Related research
Keywords: Mixed normal distribution; structural vector autoregressive model; vector autoregressive process;

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Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

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  1. Ben S. Bernanke & Ilian Mihov, 1998. "Measuring Monetary Policy," The Quarterly Journal of Economics, MIT Press, vol. 113(3), pages 869-902, August. [Downloadable!] (restricted)
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  2. Bernanke, Ben S. & Mihov, Ilian, 1998. "The liquidity effect and long-run neutrality," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 49(1), pages 149-194, December. [Downloadable!] (restricted)
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  3. Christopher A. Sims, 1986. "Are forecasting models usable for policy analysis?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-16. [Downloadable!]
  4. Roberto Rigobon, 2003. "Identification Through Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 777-792, 09. [Downloadable!] (restricted)
  5. Roberto Rigobon & Brian Sack, 2003. "Measuring The Reaction Of Monetary Policy To The Stock Market," The Quarterly Journal of Economics, MIT Press, vol. 118(2), pages 639-669, May. [Downloadable!] (restricted)
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  6. Strongin, Steven, 1995. "The identification of monetary policy disturbances explaining the liquidity puzzle," Journal of Monetary Economics, Elsevier, vol. 35(3), pages 463-497, June. [Downloadable!] (restricted)
  7. Markku Lanne & Helmut Luetkepohl, 2005. "Structural Vector Autoregressions with Nonnormal Residuals," Economics Working Papers ECO2005/25, European University Institute. [Downloadable!]
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  8. Sims, Christopher A., 1992. "Interpreting the macroeconomic time series facts : The effects of monetary policy," European Economic Review, Elsevier, vol. 36(5), pages 975-1000, June. [Downloadable!] (restricted)
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  9. Lawrence J. Christiano & Martin Eichenbaum, 1991. "Identification and the Liquidity Effect of a Monetary Policy Shock," NBER Working Papers 3920, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  10. Bernanke, Ben S & Blinder, Alan S, 1992. "The Federal Funds Rate and the Channels of Monetary Transmission," American Economic Review, American Economic Association, vol. 82(4), pages 901-21, September. [Downloadable!] (restricted)
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