Local asymptotic normality for regression models with long-memory disturbance

• 成果类型: Article
• 署名作者: Hallin, M; Taniguchi, M; Serroukh, A; Choy, K
• 署名单位: Universite Libre de Bruxelles; Universite Libre de Bruxelles; University of Osaka; Imperial College London; Komazawa University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 1999
• 页码: 2054-2080
• 关键词: time-series models; Adaptive estimation; efficient estimation; arma processes; dependence; parameter; inference; tests; trend

摘要

The local asymptotic normality property is established fur a regression model with fractional ARIMA(p, d, q) errors. This result allows for solving, in an asymptotically optimal way, a variety of inference problems In the long-memory context: hypothesis testing, discriminant analysis, rank-based testing, locally asymptotically minimax and adaptive estimation, etc. The problem of testing linear constraints on the parameters, the discriminant analysis problem, and the construction of locally asymptotically minimax adaptive estimators are treated in some detail.