| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w22976 |
| 来源ID | Working Paper 22976 |
| Classification Trees for Heterogeneous Moment-Based Models | |
| Sam Asher; Denis Nekipelov; Paul Novosad; Stephen P. Ryan | |
| 发表日期 | 2016-12-26 |
| 出版年 | 2016 |
| 语种 | 英语 |
| 摘要 | A basic problem in applied settings is that different parameters may apply to the same model in different populations. We address this problem by proposing a method using moment trees; leveraging the basic intuition of a classification tree, our method partitions the covariate space into disjoint subsets and fits a set of moments within each subspace. We prove the consistency of this estimator and show standard rates of convergence apply post-model selection. Monte Carlo evidence demonstrates the excellent small sample performance and faster-than-parametric convergence rates of the model selection step in two common empirical contexts. Finally, we showcase the usefulness of our approach by estimating heterogeneous treatment effects in a regression discontinuity design in a development setting. |
| 主题 | Econometrics ; Estimation Methods ; Development and Growth ; Development |
| URL | https://www.nber.org/papers/w22976 |
| 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/580650 |
| 推荐引用方式 GB/T 7714 | Sam Asher,Denis Nekipelov,Paul Novosad,et al. Classification Trees for Heterogeneous Moment-Based Models. 2016. |
| 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 | ||
| w22976.pdf（1440KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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