A nonlinear certainty equivalent approximation method for dynamic stochastic problems

• 成果类型: Article
• 署名作者: Cai, Yongyang; Judd, Kenneth; Steinbuks, Jevgenijs
• 署名单位: University of Chicago; Stanford University; The World Bank
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE533
• 发表日期: 2017
• 页码: 117-147
• 关键词: New Keynesian DSGE model; competitive equilibrium; parallel computing; sparse grid approximation; real business cycle model

摘要

This paper introduces a nonlinear certainty-equivalent approximation method for dynamic stochastic problems. We first introduce a novel, stable, and efficient method for computing the decision rules in deterministic dynamic economic problems. We use the results as nonlinear and global certainty-equivalent approximations for solutions to stochastic problems, and compare their accuracy to the common linear and local certainty-equivalent methods. Our examples demonstrate that this method can be applied to solve high-dimensional problems with up to 400 state variables with acceptable accuracy. This method can also be applied to solve problems with inequality constraints. These features make the nonlinear certainty-equivalent approximation method suitable for solving complex economic problems, where other algorithms, such as log-linearization, fail to produce a valid global approximation or are far less tractable.