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A Statistical Comparison of Alternative Identification Schemes for Monetary Policy Shocks

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  • Markku Lanne
  • Helmut Luetkepohl

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.

Suggested Citation

  • Markku Lanne & Helmut Luetkepohl, 2008. "A Statistical Comparison of Alternative Identification Schemes for Monetary Policy Shocks," Economics Working Papers ECO2008/23, European University Institute.
  • Handle: RePEc:eui:euiwps:eco2008/23
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    References listed on IDEAS

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    1. Markku Lanne & Helmut Lütkepohl, 2008. "Identifying Monetary Policy Shocks via Changes in Volatility," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(6), pages 1131-1149, September.
    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.
    3. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics,in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148 Elsevier.
    4. 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 901-921, September.
    5. 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.
    6. Ben S. Bernanke & Ilian Mihov, 1998. "Measuring Monetary Policy," The Quarterly Journal of Economics, Oxford University Press, vol. 113(3), pages 869-902.
    7. 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.
    8. 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.
    9. Lanne, Markku & Lütkepohl, Helmut, 2010. "Structural Vector Autoregressions With Nonnormal Residuals," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 159-168.
    10. Roberto Rigobon & Brian Sack, 2003. "Measuring The Reaction of Monetary Policy to the Stock Market," The Quarterly Journal of Economics, Oxford University Press, vol. 118(2), pages 639-669.
    11. Roberto Rigobon, 2003. "Identification Through Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 777-792, November.
    12. 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.
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    Cited by:

    1. Helmut Lütkepohl & Anton Velinov, 2016. "Structural Vector Autoregressions: Checking Identifying Long-Run Restrictions Via Heteroskedasticity," Journal of Economic Surveys, Wiley Blackwell, vol. 30(2), pages 377-392, April.
    2. Greg Hannsgen, 2012. "Infinite-variance, alpha-stable shocks in monetary SVAR," International Review of Applied Economics, Taylor & Francis Journals, vol. 26(6), pages 755-786, April.

    More about this item

    Keywords

    Mixed normal distribution; structural vector autoregressive model; vector autoregressive process;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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