| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP15411 |
| DP15411 Discrete Mixtures of Normals Pseudo Maximum Likelihood Estimators of Structural Vector Autoregressions | |
| Gabriele Fiorentini; ENRIQUE SENTANA | |
| 发表日期 | 2020-10-29 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | Likelihood inference in structural vector autoregressions with independent non-Gaussian shocks leads to parametric identification and efficient estimation at the risk of inconsistencies under distributional misspecification. We prove that autoregressive coefficients and (scaled) impact multipliers remain consistent, but the drifts and standard deviations of the shocks are generally inconsistent. Nevertheless, we show consistency when the non-Gaussian log-likelihood is a discrete scale mixture of normals in the symmetric case, or an unrestricted finite mixture more generally. Our simulation exercises compare the efficiency of these estimators to other consistent proposals. Finally, our empirical application looks at dynamic linkages between three popular volatility indices. |
| 主题 | Financial Economics |
| 关键词 | Consistency Finite normal mixtures Pseudo maximum likelihood estimators Structural models Volatility indices |
| URL | https://cepr.org/publications/dp15411 |
| 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/544399 |
| 推荐引用方式 GB/T 7714 | Gabriele Fiorentini,ENRIQUE SENTANA. DP15411 Discrete Mixtures of Normals Pseudo Maximum Likelihood Estimators of Structural Vector Autoregressions. 2020. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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