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Solution of RE Models with Anticipated Shocks and Optimal Policy

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  • Wohltmann, Hans-Werner
  • Winkler, Roland C.

Abstract

The purpose of this paper is to solve linear dynamic rational expectations models with anticipated shocks by using the generalized Schur decomposition method. We also determine the optimal unrestricted and restricted policy responses to temporary as well as permanent shocks which both are anticipated by the public. In particular, our method is useful for the analysis of optimal monetary policy in New Keynesian dynamic general equilibrium models. --

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File URL: http://econstor.eu/bitstream/10419/22048/1/EWP-2007-32.pdf
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Bibliographic Info

Paper provided by Christian-Albrechts-University of Kiel, Department of Economics in its series Economics Working Papers with number 2007,32.

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Date of creation: 2007
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Handle: RePEc:zbw:cauewp:6877

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Keywords: Anticipated Shocks; Optimal Monetary Policy; Rational Expectations; Generalized Schur Decomposition;

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  1. Svensson, Lars E.O. & Rudebusch , Glenn, 1998. "Policy Rules for Inflation Targeting," Seminar Papers 637, Stockholm University, Institute for International Economic Studies.
  2. Clarida, Richard & Galí, Jordi & Gertler, Mark, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," CEPR Discussion Papers 2139, C.E.P.R. Discussion Papers.
  3. Backus, David & Driffill, John, 1986. "The Consistency of Optimal Policy in Stochastic Rational Expectations Models," CEPR Discussion Papers 124, C.E.P.R. Discussion Papers.
  4. Klein, Paul, 2000. "Using the generalized Schur form to solve a multivariate linear rational expectations model," Journal of Economic Dynamics and Control, Elsevier, vol. 24(10), pages 1405-1423, September.
  5. Soderlind, Paul, 1999. "Solution and estimation of RE macromodels with optimal policy," European Economic Review, Elsevier, vol. 43(4-6), pages 813-823, April.
  6. Currie,David & Levine,Paul, 1993. "Rules, Reputation and Macroeconomic Policy Coordination," Cambridge Books, Cambridge University Press, number 9780521441964, November.
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