| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w27797 |
| 来源ID | Working Paper 27797 |
| Correcting for Misclassified Binary Regressors Using Instrumental Variables | |
| Steven J. Haider; Melvin Stephens Jr. | |
| 发表日期 | 2020-09-07 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | Estimators that exploit an instrumental variable to correct for misclassification in a binary regressor typically assume that the misclassification rates are invariant across all values of the instrument. We show that this assumption is invalid in routine empirical settings. We derive a new estimator that is consistent when misclassification rates vary across values of the instrumental variable. In cases where identification is weak, our moments can be combined with bounds to provide a confidence set for the parameter of interest. |
| 主题 | Econometrics ; Estimation Methods |
| URL | https://www.nber.org/papers/w27797 |
| 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/585468 |
| 推荐引用方式 GB/T 7714 | Steven J. Haider,Melvin Stephens Jr.. Correcting for Misclassified Binary Regressors Using Instrumental Variables. 2020. |
| 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 | ||
| w27797.pdf（654KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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