Asymptotically optimal estimation in misspecified time series models

• 成果类型: Article
• 署名作者: Dahlhaus, R; Wefelmeyer, W
• 署名单位: Universitat Siegen
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 1996
• 页码: 952-974
• 关键词: efficient nonparametric-estimation; stationary-processes; spectral density; linear-processes; functionals

摘要

A concept of asymptotically efficient estimation is presented when a misspecified parametric time series model is fitted to a stationary process. Efficiency of several minimum distance estimates is proved and the behavior of the Gaussian maximum likelihood estimate is studied. Furthermore, the behavior of estimates that minimize the h-step prediction enter is discussed briefly. The paper answers to some extent the question what happens when a misspecified model is fitted to time series data and one acts as if the model were true.