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Learning About the Term Structure and Optimal Rules for Inflation Targeting

  • Mewael F. Tesfaselassie
  • Eric Schaling
  • Sylvester Eijffinger

n this paper we incorporate the term structure of interest rates in a standard inflation forecast targeting framework. We find that under flexible inflation targeting and uncertainty in the degree of persistence in the economy, allowing for active learning possibilities has effects on the optimal interest rate rule followed by the central bank. For a wide range of possible initial beliefs about the unknown parameter, the dynamically optimal rule is in general more activist, in the sense of responding aggressively to the state of the economy, than the myopic rule for small to moderate deviations of the state variable from its target. On the other hand, for large deviations, the optimal policy is less activist than the myopic and the certainty equivalence policies.

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Paper provided by Economic Research Southern Africa in its series Working Papers with number 62.

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Date of creation: 2007
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Handle: RePEc:rza:wpaper:62
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  1. Mark Gertler & Jordi Gali & Richard Clarida, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," Journal of Economic Literature, American Economic Association, vol. 37(4), pages 1661-1707, December.
  2. Ellison, Martin, 2003. "The Learning Cost of Interest Rate Reversals," CEPR Discussion Papers 4135, C.E.P.R. Discussion Papers.
  3. Stephen Pollock, 2002. "Recursive Estimation in Econometrics," Working Papers 462, Queen Mary University of London, School of Economics and Finance.
  4. Eijffinger, Sylvester C W & Schaling, Eric & Verhagen, Willem, 2000. "The Term Structure of Interest Rates and Inflation Forecast Targeting," CEPR Discussion Papers 2375, C.E.P.R. Discussion Papers.
  5. Kiefer, Nicholas M & Nyarko, Yaw, 1989. "Optimal Control of an Unknown Linear Process with Learning," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 30(3), pages 571-86, August.
  6. Kaushik Mitra & James Bullard, . "Learning About Monetary Policy Rules," Discussion Papers 00/41, Department of Economics, University of York.
  7. Orphanides, Athanasios & Williams, John C., 2003. "Imperfect knowledge, inflation expectations, and monetary policy," CFS Working Paper Series 2003/40, Center for Financial Studies (CFS).
  8. Rudebusch, G. & Svensson, L.E.O., 1999. "Eurosystem Monetary Targeting: Lessons from U.S. Data," Papers 672, Stockholm - International Economic Studies.
  9. Gaspar, Vítor & Smets, Frank & Vestin, David, 2006. "Adaptive learning, persistence, and optimal monetary policy," Working Paper Series 0644, European Central Bank.
  10. Eric Schaling & James Bullard, 2005. "Monetary Policy, Determinacy, and Learnability in the Open Economy," Computing in Economics and Finance 2005 362, Society for Computational Economics.
  11. James B. Bullard, 1991. "Learning, rational expectations and policy: a summary of recent research," Review, Federal Reserve Bank of St. Louis, issue Jan, pages 50-60.
  12. Yetman, James, 2000. "Probing Potential Output: Monetary Policy, Credibility, and Optimal Learning under Uncertainty," Staff Working Papers 00-10, Bank of Canada.
  13. Svensson, Lars E.O., 1997. "Inflation Forecast Targeting: Implementing and Monitoring Inflation Targets," Seminar Papers 615, Stockholm University, Institute for International Economic Studies.
  14. Eric Schaling, James Bullard, 2001. "New economy : new policy rules?," Computing in Economics and Finance 2001 53, Society for Computational Economics.
  15. Thomas Sargent & Noah Williams & Tao Zha, 2009. "The Conquest of South American Inflation," Journal of Political Economy, University of Chicago Press, vol. 117(2), pages 211-256, 04.
  16. James B. Bullard & Eric Schaling, 2006. "Monetary policy, determinacy, and learnability in a two-block world economy," Working Papers 2006-038, Federal Reserve Bank of St. Louis.
  17. Pollock, D. S. G., 2003. "Recursive estimation in econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 37-75, October.
  18. Volker W. Wieland, 1999. "Monetary policy, parameter uncertainty and optimal learning," Finance and Economics Discussion Series 1999-48, Board of Governors of the Federal Reserve System (U.S.).
  19. Martin Ellison & Natacha Valla, 2000. "Learning, Uncertainty And Central Bank Activism In An Economy With Strategic Interactions," Computing in Economics and Finance 2000 183, Society for Computational Economics.
  20. Beck, Gunter W. & Wieland, Volker, 2002. "Learning and control in a changing economic environment," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1359-1377, August.
  21. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, June.
  22. Schaling , Eric & Eijffinger , Sylvester & Tesfaselassie , Mewael, 2004. "Heterogeneous information about the term structure, least-squares learning and optimal rules for inflation targeting," Research Discussion Papers 23/2004, Bank of Finland.
  23. Schaling, Eric, 2004. "The Nonlinear Phillips Curve and Inflation Forecast Targeting: Symmetric versus Asymmetric Monetary Policy Rules," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(3), pages 361-86, June.
  24. Levin, Andrew T. & Moessner, Richhild, 2005. "Inflation persistence and monetary policy design: an overview," Working Paper Series 0539, European Central Bank.
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