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Optimal Monetary Policy under Uncertainty in DSGE Models: A Markov Jump-Linear-Quadratic Approach

In: Monetary Policy under Uncertainty and Learning

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  • Lars E.O. Svensson

    (Central Bank of Sweden)

  • Noah Williams

    (National Bureau of Economic Research)

Abstract

We study the design of optimal monetary policy under uncertainty in a dynamic stochastic general equilibrium model. We use a Markov jump-linear-quadratic (MJLQ) approach to study policy design, proxying the uncertainty by different discrete modes in a Markov chain, and by taking mode-dependent linear-quadratic approximations of the underlying model. This allows us to apply a powerful methodology with convenient solution algorithms that we have developed. We apply our methods to a benchmark new-Keynesian model, analyzing how policy is affected by uncertainty, and how learning and active testing affect policy and losses.

(This abstract was borrowed from another version of this item.)

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Bibliographic Info

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This chapter was published in: Klaus Schmidt-Hebbel & Carl E. Walsh & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Series Editor) (ed.) Monetary Policy under Uncertainty and Learning, , chapter 3, pages 077-114, 2009.

This item is provided by Central Bank of Chile in its series Central Banking, Analysis, and Economic Policies Book Series with number v13c03pp077-114.

Handle: RePEc:chb:bcchsb:v13c03pp077-114

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  1. 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.
  2. Eijffinger, Sylvester C W & Schaling, Eric & Tesfaselassie, Mewael F., 2006. "Learning About the Term Structure and Optimal Rules for Inflation Targeting," CEPR Discussion Papers 5896, C.E.P.R. Discussion Papers.
  3. 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.
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