| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/t0194 |
| 来源ID | Technical Working Paper 0194 |
| Exact Maximum Likelihood Estimation of Observation-Driven Econometric Models | |
| Francis X. Diebold; Til Schuermann | |
| 发表日期 | 1996-04-01 |
| 出版年 | 1996 |
| 语种 | 英语 |
| 摘要 | The possibility of exact maximum likelihood estimation of many observation-driven models remains an open question. Often only approximate maximum likelihood estimation is attempted, because the unconditional density needed for exact estimation is not known in closed form. Using simulation and nonparametric density estimation techniques that facilitate empirical likelihood evaluation, we develop an exact maximum likelihood procedure. We provide an illustrative application to the estimation of ARCH models, in which we compare the sampling properties of the exact estimator to those of several competitors. We find that, especially in situations of small samples and high persistence, efficiency gains are obtained. We conclude with a discussion of directions for future research, including application of our methods to panel data models. |
| 主题 | Econometrics ; Estimation Methods |
| URL | https://www.nber.org/papers/t0194 |
| 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/562986 |
| 推荐引用方式 GB/T 7714 | Francis X. Diebold,Til Schuermann. Exact Maximum Likelihood Estimation of Observation-Driven Econometric Models. 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 | ||
| t0194.pdf（583KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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