| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP16285 |
| DP16285 Exploiting Symmetry in High-Dimensional Dynamic Programming | |
| Mahdi Ebrahimi Kahou; Jesus Fernandez-Villaverde; Jesse Perla; Arnav Sood | |
| 发表日期 | 2021-06-23 |
| 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. The ``curse of dimensionality'' is avoided due to four 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; (3) sampling methods to ensure the model fits along manifolds of interest; and (4) selecting the most generalizable over-parameterized deep learning approximation without calculating the stationary distribution or applying a transversality condition. As an application, we solve 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 nonlinear versions with aggregate shocks. Finally, we describe how our approach applies to a large class of models in economics. |
| 主题 | Monetary Economics and Fluctuations |
| 关键词 | Machine learning Dynamic programming |
| URL | https://cepr.org/publications/dp16285 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/545250 |
| 推荐引用方式 GB/T 7714 | Mahdi Ebrahimi Kahou,Jesus Fernandez-Villaverde,Jesse Perla,et al. DP16285 Exploiting Symmetry in High-Dimensional Dynamic Programming. 2021. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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