Asymptotic distribution of least square estimators for linear models with dependent errors : regular designs - Université de Nantes Accéder directement au contenu
Article Dans Une Revue Mathematical Methods of Statistics Année : 2018

Asymptotic distribution of least square estimators for linear models with dependent errors : regular designs

Sophie Dede
  • Fonction : Auteur
  • PersonId : 1021013

Résumé

In this paper, we consider the usual linear regression model in the case where the error process is assumed strictly stationary. We use a result from Hannan (1973), who proved a Central Limit Theorem for the usual least squares estimator under general conditions on the design and the error process. We show that for a large class of designs, the asymptotic covariance matrix is as simple as the independent and identically distributed case. We then estimate the covariance matrix using an estimator of the spectral density whose consistency is proved under very mild conditions.
Fichier principal
Vignette du fichier
hal_arxiv_regular_design.pdf (395.44 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01617479 , version 1 (16-10-2017)
hal-01617479 , version 2 (31-12-2018)

Identifiants

Citer

Emmanuel Caron, Sophie Dede. Asymptotic distribution of least square estimators for linear models with dependent errors : regular designs. Mathematical Methods of Statistics, 2018, 27 (4), pp.268-293. ⟨10.3103/S1066530718040026⟩. ⟨hal-01617479v2⟩
306 Consultations
114 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More