| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w25819 |
| 来源ID | Working Paper 25819 |
| Errors in the Dependent Variable of Quantile Regression Models | |
| Jerry A. Hausman; Haoyang Liu; Ye Luo; Christopher Palmer | |
| 发表日期 | 2019-05-13 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | The popular quantile regression estimator of Koenker and Bassett (1978) is biased if there is an additive error term. Approaching this problem as an errors-in-variables problem where the dependent variable suffers from classical measurement error, we present a sieve maximum-likelihood approach that is robust to left-hand side measurement error. After providing sufficient conditions for identification, we demonstrate that when the number of knots in the quantile grid is chosen to grow at an adequate speed, the sieve maximum-likelihood estimator is consistent and asymptotically normal, permitting inference via bootstrapping. We verify our theoretical results with Monte Carlo simulations and illustrate our estimator with an application to the returns to education highlighting changes over time in the returns to education that have previously been masked by measurement-error bias. |
| 主题 | Econometrics ; Estimation Methods ; Health, Education, and Welfare ; Education ; Labor Economics ; Labor Compensation |
| URL | https://www.nber.org/papers/w25819 |
| 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/583492 |
| 推荐引用方式 GB/T 7714 | Jerry A. Hausman,Haoyang Liu,Ye Luo,et al. Errors in the Dependent Variable of Quantile Regression Models. 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 | ||
| w25819.pdf（1578KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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