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A New Algorithm for Solving Dynamic Stochastic Macroeconomic Models

Author

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  • Kevin Salyer
  • Victor Dorofeenko
  • Gabriel Lee

    (Department of Economics, University of California Davis)

Abstract

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 a set of differential algebraic equations in the neighborhood of the steady-state. Then a modified recursive upwind Gauss Seidel method 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 demonstrated to be fast and globally accurate. This comparison is done within a discrete state setting with heteroskedasticity in the technology shocks. It is shown that linearization methods perform poorly in this environment even though the unconditional variance of shocks is relatively small.

Suggested Citation

  • Kevin Salyer & Victor Dorofeenko & Gabriel Lee, 2005. "A New Algorithm for Solving Dynamic Stochastic Macroeconomic Models," Working Papers 62, University of California, Davis, Department of Economics.
  • Handle: RePEc:cda:wpaper:06-2
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Olaf Posch & Timo Trimborn, 2010. "Numerical solution of continuous-time DSGE models under Poisson uncertainty," Economics Working Papers 2010-08, Department of Economics and Business Economics, Aarhus University.
    2. Rodolphe Buda, 2013. "SIMUL 3.2: An Econometric Tool for Multidimensional Modelling," Computational Economics, Springer;Society for Computational Economics, vol. 41(4), pages 517-524, April.
    3. Ikefuji, M. & Laeven, R.J.A. & Magnus, J.R. & Muris, C.H.M., 2010. "Scrap Value Functions in Dynamic Decision Problems," Discussion Paper 2010-77, Tilburg University, Center for Economic Research.
    4. Posch, Olaf & Trimborn, Timo, 2013. "Numerical solution of dynamic equilibrium models under Poisson uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2602-2622.

    More about this item

    Keywords

    numerical methods; projection methods; real business cycles;

    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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