Template-Type: ReDIF-Paper 1.0 Author-Name: Kevin Salyer Author-Name-First: Kevin Author-Name-Last: Salyer Author-Name: Victor Dorofeenko Author-Name-First: Victor Author-Name-Last: Dorofeenko Author-Name: Gabriel Lee Author-Name-First: Gabriel Author-Name-Last: Lee Author-Workplace-Name: Department of Economics, University of California Davis Title: A New Algorithm for Solving Dynamic Stochastic Macroeconomic Models 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. Length: 26 File-URL: https://repec.dss.ucdavis.edu/files/7KhhoKVNv6WxUpEHNQHaRGih/06-2.pdf File-Format: application/pdf Number: 211 Classification-JEL: C63, C68, E37 KeyWords: numerical methods, projection methods, real business cycles Creation-Date: 20051128 Handle: RePEc:cda:wpaper:211