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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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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. 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 162, Society for Computational Economics.
  2. Kim, Jinill & Kim, Sunghyun Henry, 2003. "Spurious welfare reversals in international business cycle models," Journal of International Economics, Elsevier, vol. 60(2), pages 471-500, August.
  3. Benigno, Pierpaolo & Woodford, Michael, 2004. "Optimal monetary and fiscal policy: a linear-quadratic approach," Working Paper Series 0345, European Central Bank.
  4. Lawrence J. Christiano, 1998. "Solving Dynamic Equilibrium Models by a Method of Undetermined Coefficients," NBER Technical Working Papers 0225, National Bureau of Economic Research, Inc.
  5. Pierpaolo Benigno & Michael Woodford, 2005. "Optimal stabilization policy when wages and prices are sticky: the case of a distorted steady state," Proceedings, Board of Governors of the Federal Reserve System (U.S.), pages 127-180.
  6. Schmitt-Grohe, Stephanie & Uribe, Martin, 2004. "Solving dynamic general equilibrium models using a second-order approximation to the policy function," Journal of Economic Dynamics and Control, Elsevier, vol. 28(4), pages 755-775, January.
  7. Erik Canton, 2002. "Business cycles in a two-sector model of endogenous growth," Economic Theory, Springer, vol. 19(3), pages 477-492.
  8. Uhlig, H., 1995. "A toolkit for analyzing nonlinear dynamic stochastic models easily," Discussion Paper 1995-97, Tilburg University, Center for Economic Research.
  9. 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, vol. 20(1-2), pages 57-86, October.
  10. Sutherland, Alan, 2002. "A Simple Second-Order Solution Method For Dynamic General Equilibrium Models," CEPR Discussion Papers 3554, C.E.P.R. Discussion Papers.
  11. 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.
  12. 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.
  13. Blanchard, Olivier Jean & Kahn, Charles M, 1980. "The Solution of Linear Difference Models under Rational Expectations," Econometrica, Econometric Society, vol. 48(5), pages 1305-11, July.
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