| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/t0195 |
| 来源ID | Technical Working Paper 0195 |
| Inferences from Parametric and Non-Parametric Covariance Matrix Estimation Procedures | |
| Wouter J. Den Haan; Andrew Levin | |
| 发表日期 | 1996-05-01 |
| 出版年 | 1996 |
| 语种 | 英语 |
| 摘要 | In this paper, we propose a parametric spectral estimation procedure for constructing heteroskedasticity and autocorrelation consistent (HAC) covariance matrices. We establish the consistency of this procedure under very general conditions similar to those considered in previous research, and we demonstrate that the parametric estimator converges at a faster rate than the kernel-based estimators proposed by Andrews and Monahan (1992) and Newey and West (1994). In finite samples, our Monte Carlo experiments indicate that the parametric estimator matches, and in some cases greatly exceeds, the performance of the prewhitened kernel estimator proposed by Andrews and Monahan (1992). These simulation experiments illustrate several important limitations of non-parametric HAC estimation procedures, and highlight the advantages of explicitly modeling the temporal properties of the error terms. Wouter J. den Haan Andrew Levin Depa |
| 主题 | Econometrics ; Estimation Methods |
| URL | https://www.nber.org/papers/t0195 |
| 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/563028 |
| 推荐引用方式 GB/T 7714 | Wouter J. Den Haan,Andrew Levin. Inferences from Parametric and Non-Parametric Covariance Matrix Estimation Procedures. 1996. |
| 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 | ||
| t0195.pdf（1714KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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