| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP12682 |
| DP12682 Consistent non-Gaussian pseudo maximum likelihood estimators | |
| ENRIQUE SENTANA; Gabriele Fiorentini | |
| 发表日期 | 2018-02-04 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | We characterise the mean and variance parameters that distributionally misspecified maximum likelihood estimators can consistently estimate in multivariate conditionally heteroskedastic dynamic regression models. We also provide simple closed-form consistent estimators for the rest. The inclusion of means and the explicit coverage of multivariate models make our procedures useful not only for GARCH models but also in many empirically relevant macro and finance applications involving VARs and multivariate regressions. We study the statistical properties of our proposed consistent estimators, as well as their efficiency relative to Gaussian pseudo maximum likelihood procedures. Finally, we provide finite sample results through Monte Carlo simulations. |
| 主题 | Financial Economics |
| 关键词 | Consistency Efficiency Misspecification |
| URL | https://cepr.org/publications/dp12682 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/541494 |
| 推荐引用方式 GB/T 7714 | ENRIQUE SENTANA,Gabriele Fiorentini. DP12682 Consistent non-Gaussian pseudo maximum likelihood estimators. 2018. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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