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Recovering from Crash States: A ''New'' Algorithm for Solving Dynamic Stochastic Macroeconomic Models


  • Viktor Dorofeenko


  • Gabriel S. Lee

    (University of Regensburg and IHS)

  • Kevin D. Salyer

    (UC Davis)


We introduce a ''new'' algorithm that can be used to solve stochastic dynamic general equilibrium models. This approach exploits the fact that the equations defining equilibrium can be viewed as set of algebraic equations in the neighborhood of the steady-state. Then a recursive scheme, which employes Upwind Gauss Seidel method at each step of iteration, can be used to determine the global solution. This method, within the context of a standard real business cycle model, is compared to projection, perturbation, and linearization approaches and is shown to be fast and globally accurate. Furthermore, we show that the gain in efficiency becomes more significant if the number of discrete states of the problem grows, and hence the method allows us to avoid the state space limitation. This comparison is done within a discrete state setting in which there is a low probability, crash state for the technology shock. Critically, this environment introduces heteroscedasticity in the technology shock and we show that linearization methods perform poorly in this environment even though the unconditional variance of shocks is relatively small and similar to that typically used in RBC analysis. We then use this solution method to analyze the equilibrium behavior of the crash state economy. We demonstrate that the welfare costs of a crash state are high and lead to a larger average capital stock due to precautionary savings. Also, we analyze the behavior of the term premia (both conditional and unconditional) and demonstrate how these affect the business cycle characteristics of the yield curve

Suggested Citation

  • Viktor Dorofeenko & Gabriel S. Lee & Kevin D. Salyer, 2006. "Recovering from Crash States: A ''New'' Algorithm for Solving Dynamic Stochastic Macroeconomic Models," Computing in Economics and Finance 2006 215, Society for Computational Economics.
  • Handle: RePEc:sce:scecfa:215

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    References listed on IDEAS

    1. Carl E. Walsh, 2003. "Monetary Theory and Policy, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232316, January.
    2. Bertocchi, Graziella & Spagat, Michael, 1993. "Learning, experimentation, and monetary policy," Journal of Monetary Economics, Elsevier, vol. 32(1), pages 169-183, August.
    3. Wieland, Volker, 2000. "Learning by doing and the value of optimal experimentation," Journal of Economic Dynamics and Control, Elsevier, vol. 24(4), pages 501-534, April.
    4. Basar, Tamer & Salmon, Mark, 1990. "Credibility and the value of information transmission in a model of monetary policy and inflation," Journal of Economic Dynamics and Control, Elsevier, vol. 14(1), pages 97-116, February.
    5. Wieland, Volker, 2000. "Monetary policy, parameter uncertainty and optimal learning," Journal of Monetary Economics, Elsevier, vol. 46(1), pages 199-228, August.
    6. Alan S. Blinder, 1999. "Central Banking in Theory and Practice," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262522608, January.
    7. Ghosh, Atish R & Masson, Paul R, 1991. "Model Uncertainty, Learning, and the Gains from Coordination," American Economic Review, American Economic Association, vol. 81(3), pages 465-479, June.
    8. Ronald J. Balvers & Thomas F. Cosimano, 1994. "Inflation Variability and Gradualist Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 61(4), pages 721-738.
    9. Frankel, Jeffrey A & Rockett, Katharine E, 1988. "International Macroeconomic Policy Coordination When Policymakers Do Not Agree on the True Model," American Economic Review, American Economic Association, vol. 78(3), pages 318-340, June.
    10. Svensson, Lars E O, 1999. "Price-Level Targeting versus Inflation Targeting: A Free Lunch?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 31(3), pages 277-295, August.
    11. Ellison, Martin & Vilmunen, Jouko, 2005. "A simple approach to identifying the incentives for policy experimentation," Economics Letters, Elsevier, vol. 86(2), pages 167-172, February.
    12. Kiefer, Nicholas M & Nyarko, Yaw, 1989. "Optimal Control of an Unknown Linear Process with Learning," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 30(3), pages 571-586, August.
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    More about this item


    numerical methods; Gauss Seidel method ; projection methods; real business cycles; crash state;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications


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