A New Algorithm for Solving Dynamic Stochastic Macroeconomic Models

Kevin Salyer (), Victor Dorofeenko and Gabriel Lee

No 211, Working Papers from University of California, Davis, Department of Economics

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.

Keywords: numerical methods; projection methods; real business cycles (search for similar items in EconPapers)
JEL-codes: C63 C68 E37 (search for similar items in EconPapers)
Pages: 26
Date: 2005-11-28
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Citations: View citations in EconPapers (2)

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Journal Article: A new algorithm for solving dynamic stochastic macroeconomic models (2010)
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