| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/t0344 |
| 来源ID | Technical Working Paper 0344 |
| Bootstrap-Based Improvements for Inference with Clustered Errors | |
| A. Colin Cameron; Jonah B. Gelbach; Douglas L. Miller | |
| 发表日期 | 2007-09-21 |
| 出版年 | 2007 |
| 语种 | 英语 |
| 摘要 | Researchers have increasingly realized the need to account for within-group dependence in estimating standard errors of regression parameter estimates. The usual solution is to calculate cluster-robust standard errors that permit heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. Standard asymptotic tests can over-reject, however, with few (5-30) clusters. We investigate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the example of Bertrand, Duflo and Mullainathan (2004). Rejection rates of ten percent using standard methods can be reduced to the nominal size of five percent using our methods. |
| 主题 | Econometrics ; Estimation Methods |
| URL | https://www.nber.org/papers/t0344 |
| 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/571094 |
| 推荐引用方式 GB/T 7714 | A. Colin Cameron,Jonah B. Gelbach,Douglas L. Miller. Bootstrap-Based Improvements for Inference with Clustered Errors. 2007. |
| 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 | ||
| t0344.pdf（370KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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