| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w29953 |
| 来源ID | Working Paper 29953 |
| Partially Linear Models under Data Combination | |
| Xavier D'; Haultfoeuille; Christophe Gaillac; Arnaud Maurel | |
| 发表日期 | 2022-04-18 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | We consider the identification of and inference on a partially linear model, when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked. This type of data combination problem arises very frequently in empirical microeconomics. Using recent tools from optimal transport theory, we derive a constructive characterization of the sharp identified set. We then build on this result and develop a novel inference method that exploits the specific geometric properties of the identified set. Our method exhibits good performances in finite samples, while remaining very tractable. Finally, we apply our methodology to study intergenerational income mobility over the period 1850-1930 in the United States. Our method allows to relax the exclusion restrictions used in earlier work while delivering confidence regions that are informative. |
| 主题 | Econometrics ; Estimation Methods ; Labor Economics ; Unemployment and Immigration |
| URL | https://www.nber.org/papers/w29953 |
| 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/587627 |
| 推荐引用方式 GB/T 7714 | Xavier D',Haultfoeuille,Christophe Gaillac,et al. Partially Linear Models under Data Combination. 2022. |
| 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 | ||
| w29953.pdf（707KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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