| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w16928 |
| 来源ID | Working Paper 16928 |
| Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST) | |
| Bryan S. Graham; Cristine Campos de Xavier Pinto; Daniel Egel | |
| 发表日期 | 2011-04-07 |
| 出版年 | 2011 |
| 语种 | 英语 |
| 摘要 | We propose a locally efficient estimator for a class of semiparametric data combination problems. A leading estimand in this class is the Average Treatment Effect on the Treated (ATT). Data combination problems are related to, but distinct from, the class of missing data problems analyzed by Robins, Rotnitzky and Zhao (1994) (of which the Average Treatment Effect (ATE) estimand is a special case). Our estimator also possesses a double robustness property. Our procedure may be used to efficiently estimate, among other objects, the ATT, the two-sample instrumental variables model (TSIV), counterfactual distributions, poverty maps, and semiparametric difference-in-differences. In an empirical application we use our procedure to characterize residual Black-White wage inequality after flexibly controlling for 'pre-market' differences in measured cognitive achievement as in Neal and Johnson (1996). |
| 主题 | Econometrics ; Estimation Methods ; Labor Economics ; Labor Compensation ; Labor Discrimination |
| URL | https://www.nber.org/papers/w16928 |
| 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/574603 |
| 推荐引用方式 GB/T 7714 | Bryan S. Graham,Cristine Campos de Xavier Pinto,Daniel Egel. Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST). 2011. |
| 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 | ||
| w16928.pdf（409KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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