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Heterogeneous information about the term structure, least-squares learning and optimal rules for inflation targeting

  • Schaling , Eric

    ()

    (Department of Economics, RAU and CentER for Economic Research, Tilburg University)

  • Eijffinger , Sylvester

    ()

    (CentER for Economic Research, Tilburg University and CEPR)

  • Tesfaselassie , Mewael

    (Tilburg University)

In this paper we incorporate the term structure of interest rates into a standard inflation forecast targeting framework. Learning about the transmission process of monetary policy is introduced by having heterogeneous agents – ie central bank and private agents – who have different information sets about the future sequence of short-term interest rates. We analyse inflation forecast targeting in two environments. One in which the central bank has perfect knowledge, in the sense that it understands and observes the process by which private sector interest rate expectations are generated, and one in which the central bank has imperfect knowledge. In the case of imperfect knowledge, the central bank has to learn about private sector interest rate expectations, as the latter affect the impact of monetary policy through the expectations theory of the term structure of interest rates. Here, following Evans and Honkapohja (2001), the learning scheme we investigate is that of least-squares learning (recursive OLS) using the Kalman filter. We find that optimal monetary policy under learning is a policy that separates estimation and control. Therefore, this model suggests that the practical relevance of the breakdown of the separation principle and the need for experimentation in policy may be limited.

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File URL: http://www.suomenpankki.fi/en/julkaisut/tutkimukset/keskustelualoitteet/Documents/0423.pdf
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Paper provided by Bank of Finland in its series Research Discussion Papers with number 23/2004.

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Length: 53 pages
Date of creation: 13 Oct 2004
Date of revision:
Handle: RePEc:hhs:bofrdp:2004_023
Contact details of provider: Postal: Bank of Finland, P.O. Box 160, FI-00101 Helsinki, Finland
Web page: http://www.suomenpankki.fi/en/

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  1. Eijffinger, S.C.W. & Schaling, E. & Verhagen, W.H., 1998. "The Term Structure of Interest Rates and Inflation Forecast Targeting," Discussion Paper 1998-85, Tilburg University, Center for Economic Research.
  2. Eric Schaling, 2004. "Learning, inflation expectations and optimal monetary policy," Macroeconomics 0404035, EconWPA.
  3. James Bullard & Eric Schaling, 2000. "New economy - new policy rules?," Working Papers 2000-019, Federal Reserve Bank of St. Louis.
  4. 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.
  5. Athanasios Orphanides & John C. Williams, 2003. "Imperfect Knowledge, Inflation Expectations, and Monetary Policy," NBER Working Papers 9884, National Bureau of Economic Research, Inc.
  6. Richard Clarida & Jordi Gali & Mark Gertler, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," NBER Working Papers 7147, National Bureau of Economic Research, Inc.
  7. Eric Schaling, 1999. "The non-linear Phillips curve and inflation forecast targeting," Bank of England working papers 98, Bank of England.
  8. Kaushik Mitra, . "Desirability of Nominal GDP Targeting Under Adaptive Learning," Discussion Papers 00/60, Department of Economics, University of York.
  9. 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.
  10. Fuhrer, Jeffrey C & Moore, George R, 1995. "Monetary Policy Trade-offs and the Correlation between Nominal Interest Rates and Real Output," American Economic Review, American Economic Association, vol. 85(1), pages 219-39, March.
  11. Taylor, John B., 1993. "Discretion versus policy rules in practice," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 39(1), pages 195-214, December.
  12. Jovanovic, Boyan & Nyarko, Yaw, 1996. "Learning by Doing and the Choice of Technology," Econometrica, Econometric Society, vol. 64(6), pages 1299-1310, November.
  13. Rudebusch, Glenn D. & Svensson, Lars E. O., 2002. "Eurosystem monetary targeting: Lessons from U.S. data," European Economic Review, Elsevier, vol. 46(3), pages 417-442, March.
  14. Volker 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.).
  15. 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.
  16. Foster, Andrew D & Rosenzweig, Mark R, 1995. "Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture," Journal of Political Economy, University of Chicago Press, vol. 103(6), pages 1176-1209, December.
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