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Computing Second-Order-Accurate Solutions for Rational Expectation Models Using Linear Solution Methods

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  • Giovanni Lombardo

    ()

  • Alan Sutherland

    ()

Abstract

This paper shows how to compute a second-order accurate solution of a non-linear rational expectation model using algorithms developed for the solution of linear rational expectation models. This result is a state-space representation for the realized values of the variables of the model. This state-space representation can easily be used to compute impulse responses as well as conditional and unconditional forecasts.

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File URL: http://www.st-andrews.ac.uk/economics/CDMA/papers/cp0504.pdf
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Bibliographic Info

Paper provided by Centre for Dynamic Macroeconomic Analysis in its series CDMA Conference Paper Series with number 0504.

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Date of creation: Mar 2005
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Handle: RePEc:san:cdmacp:0504

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Postal: Department of Economics, University of St. Andrews, Fife KY16 9AL
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Web page: http://www.st-andrews.ac.uk/cdma
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Keywords: Second-order approximation; solution method for rational expectation models.;

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  1. Pierpaolo Benigno & Michael Woodford, 2004. "Optimal Stabilization Policy When Wages and Prices are Sticky: The Case of a Distorted Steady State," NBER Working Papers 10839, National Bureau of Economic Research, Inc.
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  3. Christopher A. Sims & Jinill Kim & Sunghyun Kim, 2003. "Calculating and Using Second Order Accurate Solution of Discrete Time Dynamic Equilibrium Models," Computing in Economics and Finance 2003, Society for Computational Economics 162, Society for Computational Economics.
  4. Jinill Kim & Sunghyun Kim & Ernst Schaumburg & Christopher A. Sims, 2003. "Calculating and Using Second Order Accurate Solutions of Discrete Time," Levine's Bibliography 666156000000000284, UCLA Department of Economics.
  5. Pierpaolo Benigno & Michael Woodford, 2003. "Optimal Monetary and Fiscal Policy: A Linear Quadratic Approach," NBER Working Papers 9905, National Bureau of Economic Research, Inc.
  6. King, Robert G & Watson, Mark W, 2002. "System Reduction and Solution Algorithms for Singular Linear Difference Systems under Rational Expectations," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 20(1-2), pages 57-86, October.
  7. Christiano, Lawrence J, 2002. "Solving Dynamic Equilibrium Models by a Method of Undetermined Coefficients," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 20(1-2), pages 21-55, October.
  8. Schmitt-Grohé, Stephanie & Uribe, Martín, 2001. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function," CEPR Discussion Papers, C.E.P.R. Discussion Papers 2963, C.E.P.R. Discussion Papers.
  9. Sutherland, Alan, 2002. "A Simple Second-Order Solution Method For Dynamic General Equilibrium Models," CEPR Discussion Papers, C.E.P.R. Discussion Papers 3554, C.E.P.R. Discussion Papers.
  10. Harald Uhlig, 1995. "A toolkit for analyzing nonlinear dynamic stochastic models easily," Discussion Paper / Institute for Empirical Macroeconomics, Federal Reserve Bank of Minneapolis 101, Federal Reserve Bank of Minneapolis.
  11. Klein, Paul, 2000. "Using the generalized Schur form to solve a multivariate linear rational expectations model," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 24(10), pages 1405-1423, September.
  12. Canton, E.J.F., 1996. "Business Cycles in a Two-Sector Model of Endogenous Growth," Discussion Paper, Tilburg University, Center for Economic Research 1996-116, Tilburg University, Center for Economic Research.
  13. Blanchard, Olivier Jean & Kahn, Charles M, 1980. "The Solution of Linear Difference Models under Rational Expectations," Econometrica, Econometric Society, Econometric Society, vol. 48(5), pages 1305-11, July.
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