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Computing second-order-accurate solutions for rational expectation models using linear solution methods

  • Lombardo, Giovanni
  • Sutherland, Alan

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. The 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. JEL Classification: C63, E0

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Paper provided by European Central Bank in its series Working Paper Series with number 0487.

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Date of creation: May 2005
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Handle: RePEc:ecb:ecbwps:20050487
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  1. Christiano, Lawrence J, 2002. "Solving Dynamic Equilibrium Models by a Method of Undetermined Coefficients," Computational Economics, Society for Computational Economics, vol. 20(1-2), pages 21-55, October.
  2. 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.
  3. 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.
  4. 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.
  5. Pierpaolo Benigno & Michael Woodford, 2004. "Optimal monetary and fiscal policy: a linear-quadratic approach," International Finance Discussion Papers 806, Board of Governors of the Federal Reserve System (U.S.).
  6. Alan Sutherland, 2002. "A Simple Second-Order Solution Method for Dynamic General Equilibrium Models," Discussion Paper Series, Department of Economics 200211, Department of Economics, University of St. Andrews.
  7. 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.
  8. Harald Uhlig, 1998. "A Toolkit for Analysing Nonlinear Dynamic Stochastic Models Easily," QM&RBC Codes 123, Quantitative Macroeconomics & Real Business Cycles.
  9. 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.
  10. Canton, E.J.F., 1996. "Business Cycles in a Two-Sector Model of Endogenous Growth," Discussion Paper 1996-116, Tilburg University, Center for Economic Research.
  11. 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.
  12. 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.
  13. 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.
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