Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31371
Authors: 
Salyer, Kevin D.
Dorofeenko, Victor
Lee, Gabriel
Year of Publication: 
2005
Series/Report no.: 
Working papers // University of California, Department of Economics 06,2
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.
JEL: 
C63
C68
E37
Document Type: 
Working Paper

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