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Learning and optimal monetary policy

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  • Richard Dennis
  • Federico Ravenna

Abstract

To conduct policy efficiently, central banks must use available data to infer, or learn, the relevant structural relationships in the economy. However, because a central bank's policy affects economic outcomes, the chosen policy may help or hinder its efforts to learn. This paper examines whether real-time learning allows a central bank to learn the economy's underlying structure and studies the impact that learning has on the performance of optimal policies under a variety of learning environments. Our main results are as follows. First, when monetary policy is formulated as an optimal discretionary targeting rule, we find that the rational expectations equilibrium and the optimal policy are real-time learnable. This result is robust to a range of assumptions concerning private sector learning behavior. Second, when policy is set with discretion, learning can lead to outcomes that are better than if the model parameters are known. Finally, if the private sector is learning, then unannounced changes to the policy regime, particularly changes to the inflation target, can raise policy loss considerably.

Suggested Citation

  • Richard Dennis & Federico Ravenna, 2007. "Learning and optimal monetary policy," Working Paper Series 2007-19, Federal Reserve Bank of San Francisco.
  • Handle: RePEc:fip:fedfwp:2007-19
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Richard Dennis, 2007. "Model uncertainty and monetary policy," Working Paper Series 2007-09, Federal Reserve Bank of San Francisco.
    2. Matthes, Christian & Rondina, Francesca, 2017. "Two-sided learning and short-run dynamics in a New Keynesian model of the economy," Economics Letters, Elsevier, vol. 159(C), pages 53-56.
    3. Bianchi, Francesco & Mumtaz, Haroon & Surico, Paolo, 2009. "The great moderation of the term structure of UK interest rates," Journal of Monetary Economics, Elsevier, vol. 56(6), pages 856-871, September.
    4. George W. Evans & Seppo Honkapohja, 2009. "Expectations, Learning and Monetary Policy: An Overview of Recent Research," Central Banking, Analysis, and Economic Policies Book Series,in: Klaus Schmidt-Hebbel & Carl E. Walsh & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Series (ed.), Monetary Policy under Uncertainty and Learning, edition 1, volume 13, chapter 2, pages 027-076 Central Bank of Chile.
    5. Di Giorgio, Giorgio & Traficante, Guido, 2013. "The loss from uncertainty on policy targets," Economic Modelling, Elsevier, vol. 30(C), pages 175-182.
    6. Federico Ravenna, 2014. "How Central Banks Learn the True Model of the Economy," Cahiers de recherche 1409, CIRPEE.
    7. Dennis, Richard, 2010. "How robustness can lower the cost of discretion," Journal of Monetary Economics, Elsevier, vol. 57(6), pages 653-667, September.
    8. Ravenna, Federico, 2012. "Optimal monetary policy and model selection in a real-time learning environment," Economics Letters, Elsevier, vol. 114(3), pages 322-325.
    9. Dieppe, Alistair & Pandiella, Alberto González & Hall, Stephen & Willman, Alpo, 2013. "Limited information minimal state variable learning in a medium-scale multi-country model," Economic Modelling, Elsevier, vol. 33(C), pages 808-825.
    10. Brzoza-Brzezina, Michal & Kot, Adam, 2008. "The Relativity Theory Revisited: Is Publishing Interest Rate Forecasts Really so Valuable?," MPRA Paper 10296, University Library of Munich, Germany.
    11. repec:kap:compec:v:50:y:2017:i:3:d:10.1007_s10614-016-9601-4 is not listed on IDEAS
    12. Lahcen, BOUNADER, 2016. "Optimal Monetary Policy in Behavioral New Keynesian Model," MPRA Paper 74743, University Library of Munich, Germany.

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