| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w28981 |
| 来源ID | Working Paper 28981 |
| Exploiting Symmetry in High-Dimensional Dynamic Programming | |
| Mahdi Ebrahimi Kahou; Jesús Fernández-Villaverde; Jesse Perla; Arnav Sood | |
| 发表日期 | 2021-07-05 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | We propose a new method for solving high-dimensional dynamic programming problems and recursive competitive equilibria with a large (but finite) number of heterogeneous agents using deep learning. We avoid the curse of dimensionality thanks to three complementary techniques: (1) exploiting symmetry in the approximate law of motion and the value function; (2) constructing a concentration of measure to calculate high-dimensional expectations using a single Monte Carlo draw from the distribution of idiosyncratic shocks; and (3) designing and training deep learning architectures that exploit symmetry and concentration of measure. As an application, we find a global solution of a multi-firm version of the classic Lucas and Prescott (1971) model of investment under uncertainty. First, we compare the solution against a linear-quadratic Gaussian version for validation and benchmarking. Next, we solve the nonlinear version where no accurate or closed-form solution exists. Finally, we describe how our approach applies to a large class of models in economics. |
| 主题 | Econometrics ; Macroeconomics |
| URL | https://www.nber.org/papers/w28981 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/586655 |
| 推荐引用方式 GB/T 7714 | Mahdi Ebrahimi Kahou,Jesús Fernández-Villaverde,Jesse Perla,et al. Exploiting Symmetry in High-Dimensional Dynamic Programming. 2021. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w28981.pdf（967KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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