| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w21124 |
| 来源ID | Working Paper 21124 |
| Improving Policy Functions in High-Dimensional Dynamic Games | |
| Carlos A. Manzanares; Ying Jiang; Patrick Bajari | |
| 发表日期 | 2015-05-04 |
| 出版年 | 2015 |
| 语种 | 英语 |
| 摘要 | In this paper, we propose a method for finding policy function improvements for a single agent in high-dimensional Markov dynamic optimization problems, focusing in particular on dynamic games. Our approach combines ideas from literatures in Machine Learning and the econometric analysis of games to derive a one-step improvement policy over any given benchmark policy. In order to reduce the dimensionality of the game, our method selects a parsimonious subset of state variables in a data-driven manner using a Machine Learning estimator. This one-step improvement policy can in turn be improved upon until a suitable stopping rule is met as in the classical policy function iteration approach. We illustrate our algorithm in a high-dimensional entry game similar to that studied by Holmes (2011) and show that it results in a nearly 300 percent improvement in expected profits as compared with a benchmark policy. |
| 主题 | Econometrics ; Estimation Methods ; Microeconomics ; Game Theory ; Industrial Organization ; Market Structure and Firm Performance |
| URL | https://www.nber.org/papers/w21124 |
| 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/578799 |
| 推荐引用方式 GB/T 7714 | Carlos A. Manzanares,Ying Jiang,Patrick Bajari. Improving Policy Functions in High-Dimensional Dynamic Games. 2015. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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