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Learning, inflation expectations and optimal monetary policy

  • Schaling, Eric

    (Department of Economics, RAU)

In this paper we analyse disinflation policy in two environments. In the first, the central bank has perfect knowledge, in the sense that it understands and observes the process by which private sector inflation expectations are generated; in the second, the central bank has to learn the private sector inflation forecasting rule. With imperfect knowledge, results depend on the learning scheme that is employed. Here, the learning scheme we investigate is that of least-squares learning (recursive OLS) using the Kalman filter. A novel feature of a learning-based policy – as against the central bank’s disinflation policy under perfect knowledge – is that the degree of monetary accommodation (the extent to which the central bank accommodates private sector inflation expectations) is no longer constant across the disinflation, but becomes state-dependent. This means that the central bank’s behaviour changes during the disinflation as it collects more information.

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File URL: http://www.suomenpankki.fi/en/julkaisut/tutkimukset/keskustelualoitteet/Documents/0320.pdf
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Paper provided by Bank of Finland in its series Research Discussion Papers with number 20/2003.

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Length: 53 pages
Date of creation: 18 Aug 2003
Date of revision:
Handle: RePEc:hhs:bofrdp:2003_020
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. Wieland, Volker, 1999. "Monetary policy, parameter uncertainty and optimal learning," ZEI Working Papers B 09-1999, ZEI - Center for European Integration Studies, University of Bonn.
  2. Eric Schaling & Marco Hoeberichts, 2010. "Why Speed Doesn’t Kill: Learning to Believe in Disinflation," De Economist, Springer, vol. 158(1), pages 23-42, April.
  3. David Andolfatto & Scott Hendry & Kevin Moran, 2004. "Inflation Expectations and Learning about Monetary Policy," DNB Staff Reports (discontinued) 121, Netherlands Central Bank.
  4. 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.
  5. 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.
  6. 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.
  7. Marcet, Albert & Sargent, Thomas J, 1989. "Convergence of Least-Squares Learning in Environments with Hidden State Variables and Private Information," Journal of Political Economy, University of Chicago Press, vol. 97(6), pages 1306-22, December.
  8. Bertocchi, Graziella & Spagat, Michael, 1993. "Learning, experimentation, and monetary policy," Journal of Monetary Economics, Elsevier, vol. 32(1), pages 169-183, August.
  9. Mervyn King, 1996. "How should central banks reduce inflation? - Conceptual issues," Economic Review, Federal Reserve Bank of Kansas City, issue Q IV, pages 25-52.
  10. Eric Schaling, 1999. "The non-linear Phillips curve and inflation forecast targeting," Bank of England working papers 98, Bank of England.
  11. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  12. Marcet, Albert & Sargent, Thomas J., 1989. "Convergence of least squares learning mechanisms in self-referential linear stochastic models," Journal of Economic Theory, Elsevier, vol. 48(2), pages 337-368, August.
  13. Marcet, Albert & Sargent, Thomas J, 1988. "The Fate of Systems with "Adaptive" Expectations," American Economic Review, American Economic Association, vol. 78(2), pages 168-72, May.
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