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Tracking Greenspan: Systematic and Unsystematic Monetary Policy Revisited

  • Giannone, Domenico
  • Reichlin, Lucrezia
  • Sala, Luca

This Paper proposes a new framework to analyse systematic and unsystematic monetary policy within the same econometric model. As in Bernanke and Boivin, 2001, the model aims at capturing the following facts: monetary authorities use information from a large number of data series to extract a signal on current economic activity, which is typically measured with error. Due to strong collinearity between macroeconomic time series, relevant information is obtained by regressing the observables on few aggregates. Collinearity implies that a large panel of time series, which constitutes the information available to policy makers, can be represented as a dynamic factor model {it a la} Forni and Reichlin, 1998, Stock and Watson, 1999 and Forni et al., 2000. Here we show how, in this framework, shocks can be identified structurally and the parameters of monetary policy rules, conditional on these shocks, can be estimated. Our results for the US economy between 1982 and 2001 show that: (i) Two shocks capture 80% of the variance of key variables such as output and inflation at all horizons; (ii) The monetary shock mainly affects the term structure of interest rates, but has virtually no effect on output and inflation so that monetary policy affects the economy through its systematic behavior rather than by surprising agents; (iii) Since demand and technology have been the main forces for the dynamics of cyclical output and inflation during the Greenspan era, while supply shocks have been negligible, monetary authorities did not face any tradeoff between inflation and output. By stabilizing inflation conditionally on demand shocks, they also achieved output stabilization; (iv) Conditionally on demand, Greenspan followed the Taylor principle while, conditionally on technology, monetary policy did not respond; (v) Systematic monetary policy had a substantial role in output and inflation stabilization.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 3550.

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Date of creation: Sep 2002
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Handle: RePEc:cpr:ceprdp:3550
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  1. Faust, Jon & Rogers, John H & Wright, Jonathan H, 2005. "News and Noise in G-7 GDP Announcements," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 403-19, June.
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  7. Woodford, Michael, 1999. "Optimal Monetary Policy Inertia," Manchester School, University of Manchester, vol. 67(0), pages 1-35, Supplemen.
  8. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
  9. Faust, Jon, 1998. "The robustness of identified VAR conclusions about money," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 49(1), pages 207-244, December.
  10. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 1998. "Monetary Policy Shocks: What Have We Learned and to What End?," NBER Working Papers 6400, National Bureau of Economic Research, Inc.
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  12. Galí, Jordi & Lopez-Salido, Jose David & Vallés Liberal, Javier, 2002. "Technology Shocks and Monetary Policy: Assessing the Fed's Performance," CEPR Discussion Papers 3211, C.E.P.R. Discussion Papers.
  13. Jon Faust, 1998. "The robustness of identified VAR conclusions about money," International Finance Discussion Papers 610, Board of Governors of the Federal Reserve System (U.S.).
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  17. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
  18. Athanasios Orphanides, 1998. "Monetary policy rules based on real-time data," Finance and Economics Discussion Series 1998-03, Board of Governors of the Federal Reserve System (U.S.).
  19. Forni, Mario & Reichlin, Lucrezia, 1998. "Let's Get Real: A Factor Analytical Approach to Disaggregated Business Cycle Dynamics," Review of Economic Studies, Wiley Blackwell, vol. 65(3), pages 453-73, July.
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  21. Forni, Mario & Lippi, Marco, 2000. "The Generalized Dynamic Factor Model: Representation Theory," CEPR Discussion Papers 2509, C.E.P.R. Discussion Papers.
  22. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
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