A new algorithm for solving dynamic stochastic macroeconomic models

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.