Residual empirical processes for long and short memory time series
Abstract
This paper studies the residual empirical process of long- and short-memory time series regression models and establishes its uniform expansion under a general framework. The results are applied to the stochastic regression models and unstable autoregressive models. For the long-memory noise, it is shown that the limit distribution of the Kolmogorov-Smirnov test statistic studied in Ho and Hsing [Ann. Statist. 24 (1996) 992-1024] does not hold when the stochastic regression model includes an unknown intercept or when the characteristic polynomial of the unstable autoregressive model has a unit root. To this end, two new statistics are proposed to test for the distribution of the long-memory noises of stochastic regression models and unstable autoregressive models. (With Correction.)
Cite
@article{arxiv.0811.0697,
title = {Residual empirical processes for long and short memory time series},
author = {Ngai Hang Chan and Shiqing Ling},
journal= {arXiv preprint arXiv:0811.0697},
year = {2012}
}
Comments
Published in at http://dx.doi.org/10.1214/07-AOS543 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)