In this paper we construct an efficient weighted least squares estimator for the mean and more generally for the regression parameters in certain Gaussian long range dependent regression models, including polynomial regression. The form of the estimator does not depend on the whole dependence structure of the residuals, but only on the local behaviour of the spectral density at zero. By using an estimator of the self-similarity parameter, we give a fully efficient estimator. Furthermore, we construct efficient weighted $M$-estimators.
Publié le : 1995-06-14
Classification:
Long range dependence,
weighted least squares estimates,
$M$-estimates,
efficiency,
62F12,
60F99,
62M10
@article{1176324635,
author = {Dahlhaus, Rainer},
title = {Efficient Location and Regression Estimation for Long Range Dependent Regression Models},
journal = {Ann. Statist.},
volume = {23},
number = {6},
year = {1995},
pages = { 1029-1047},
language = {en},
url = {http://dml.mathdoc.fr/item/1176324635}
}
Dahlhaus, Rainer. Efficient Location and Regression Estimation for Long Range Dependent Regression Models. Ann. Statist., Tome 23 (1995) no. 6, pp. 1029-1047. http://gdmltest.u-ga.fr/item/1176324635/