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

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  • Lars E.O. Svensson
  • Noah Williams

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.

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Bibliographic Info

Paper provided by Central Bank of Chile in its series Working Papers Central Bank of Chile with number 484.

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Date of creation: Sep 2008
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Handle: RePEc:chb:bcchwp:484

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  1. 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.
  2. Tesfaselassie, M.F. & Schaling, E. & Eijffinger, S.C.W., 2006. "Learning about the Term Structure and Optimal Rules for Inflation Targeting," Discussion Paper 2006-88, Tilburg University, Center for Economic Research.
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