| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w26566 |
| 来源ID | Working Paper 26566 |
| Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations | |
| Susan Athey; Guido W. Imbens; Jonas Metzger; Evan M. Munro | |
| 发表日期 | 2019-12-23 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | When researchers develop new econometric methods it is common practice to compare the performance of the new methods to those of existing methods in Monte Carlo studies. The credibility of such Monte Carlo studies is often limited because of the freedom the researcher has in choosing the design. In recent years a new class of generative models emerged in the machine learning literature, termed Generative Adversarial Networks (GANs) that can be used to systematically generate artificial data that closely mimics real economic datasets, while limiting the degrees of freedom for the researcher and optionally satisfying privacy guarantees with respect to their training data. In addition if an applied researcher is concerned with the performance of a particular statistical method on a specific data set (beyond its theoretical properties in large samples), she may wish to assess the performance, e.g., the coverage rate of confidence intervals or the bias of the estimator, using simulated data which resembles her setting. Tol illustrate these methods we apply Wasserstein GANs (WGANs) to compare a number of different estimators for average treatment effects under unconfoundedness in three distinct settings (corresponding to three real data sets) and present a methodology for assessing the robustness of the results. In this example, we find that (i) there is not one estimator that outperforms the others in all three settings, so researchers should tailor their analytic approach to a given setting, and (ii) systematic simulation studies can be helpful for selecting among competing methods in this situation. |
| 主题 | Econometrics ; Estimation Methods |
| URL | https://www.nber.org/papers/w26566 |
| 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/584240 |
| 推荐引用方式 GB/T 7714 | Susan Athey,Guido W. Imbens,Jonas Metzger,et al. Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations. 2019. |
| 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 | ||
| w26566.pdf（424KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
