A simple but powerful simulated certainty equivalent approximation method for dynamic stochastic problems

• 成果类型: Article
• 署名作者: Cai, Yongyang; Judd, Kenneth L.
• 署名单位: University System of Ohio; Ohio State University; Stanford University
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE1835
• 发表日期: 2023
• 页码: 651-687
• 关键词: Stochastic dynamic programming; competitive equilibrium; large-scale model; integrated assessment model; new Keynesian model; zero lower bound; occasionally binding constraint; nonstationary problem; parallelism; C61; C63; C68; Q54; Q58

摘要

We introduce a novel simulated certainty equivalent approximation (SCEQ) method for solving dynamic stochastic problems. Our examples show that SCEQ can quickly solve high-dimensional finite- or infinite-horizon, stationary or nonstationary dynamic stochastic problems with hundreds of state variables, a wide state space, and occasionally binding constraints. With the SCEQ method, a desktop computer will suffice for large problems, but it can also use parallel tools efficiently. The SCEQ method is simple, stable, and can utilize any solver, making it suitable for solving complex economic problems that cannot be solved by other algorithms.