A new algorithm for solving dynamic stochastic macroeconomic models

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 06-2

Classification and Topics

• Classification: Wirtschaft ; Computational Techniques; Simulation Modeling ; Computable General Equilibrium Models ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Salyer, Kevin D. ; Dorofeenko, Victor ; Lee, Gabriel
• Event: Veröffentlichung
• (who): University of California, Department of Economics
• (where): Davis, CA
• (when): 2005

Further information

• Handle: http://hdl.handle.net/10419/31371
• Last update: 10.03.2025, 11:45 AM CET