Adaptive semiparametric wavelet estimator and goodness-of-fit test for long memory linear processes
Statistics Theory
2010-12-08 v1 Statistics Theory
Abstract
This paper is first devoted to study an adaptive wavelet based estimator of the long memory parameter for linear processes in a general semi-parametric frame. This is an extension of Bardet {\it et al.} (2008) which only concerned Gaussian processes. Moreover, the definition of the long memory parameter estimator is modified and asymptotic results are improved even in the Gaussian case. Finally an adaptive goodness-of-fit test is also built and easy to be employed: it is a chi-square type test. Simulations confirm the interesting properties of consistency and robustness of the adaptive estimator and test.
Keywords
Cite
@article{arxiv.1012.0690,
title = {Adaptive semiparametric wavelet estimator and goodness-of-fit test for long memory linear processes},
author = {Jean-Marc Bardet and Hatem Bibi},
journal= {arXiv preprint arXiv:1012.0690},
year = {2010}
}