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Optimal Disinflation Under Learning

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  • Christian Matthes

    (Universitat Pompeu Fabra and Barcelona GSE)

  • Argia M. Sbordone

    (Federal Reserve Bank of New York)

  • Timothy Cogley

    (New York University)

Abstract

We model transitional dynamics that emerge after the adoption of a new monetary-policy rule. We assume that private agents learn about the new policy via Bayesian updating, and we study how learning affects the nature of the transition and choice of a new rule. The model endogenously generates time-varying volatility during the transition. Managing this volatility is the central bank's main challenge. The optimal policy depends on subtle features of the private sector's prior. Nevertheless, two robust conclusions emerge from our examples. First, the central bank can adjust target inflation freely without triggering high volatility. Second, none of our examples rationalizes a gradual reduction in inflation. On the contrary, inflation falls sharply at impact, overshoots the new target, and converges from below.

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Bibliographic Info

Paper provided by Society for Economic Dynamics in its series 2011 Meeting Papers with number 74.

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Date of creation: 2011
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Handle: RePEc:red:sed011:74

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  17. 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.
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