Local asymptotic normality for regression models with long-memory disturbance

Marc Hallin*, Masanobu Taniguchi, Abdeslam Serroukh, Kokyo Choy

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


The local asymptotic normality property is established for 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, rankbased 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.

Original languageEnglish
Pages (from-to)2054-2080
Number of pages27
JournalAnnals of Statistics
Issue number6
Publication statusPublished - 1999 Dec
Externally publishedYes


  • Adaptive estimation
  • Discriminant analysis
  • FARIMA model
  • Local asymptotic normality
  • Locally asymptotically optimal test
  • Long-memory process

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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