| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w16127 |
| 来源ID | Working Paper 16127 |
| A Score Based Approach to Wild Bootstrap Inference | |
| Patrick M. Kline; Andres Santos | |
| 发表日期 | 2010-06-24 |
| 出版年 | 2010 |
| 语种 | 英语 |
| 摘要 | We propose a generalization of the wild bootstrap of Wu (1986) and Liu (1988) based upon perturbing the scores of M-estimators. This "score bootstrap" procedure avoids recomputing the estimator in each bootstrap iteration, making it substantially less costly to compute than the conventional nonparametric bootstrap, particularly in complex nonlinear models. Despite this computational advantage, in the linear model, the score bootstrap studentized test statistic is equivalent to that of the conventional wild bootstrap up to order O_p(n^(-1)). We establish the consistency of the procedure for Wald and Lagrange Multiplier type tests and tests of moment restrictions for a wide class of M-estimators under clustering and potential misspecification. In an extensive series of Monte Carlo experiments we find that the performance of the score bootstrap is comparable to competing approaches despite its computational savings. |
| 主题 | Econometrics ; Estimation Methods |
| URL | https://www.nber.org/papers/w16127 |
| 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/573801 |
| 推荐引用方式 GB/T 7714 | Patrick M. Kline,Andres Santos. A Score Based Approach to Wild Bootstrap Inference. 2010. |
| 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 | ||
| w16127.pdf（572KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
