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