Distribution free goodness-of-fit tests for linear processes
Abstract
This article proposes a class of goodness-of-fit tests for the autocorrelation function of a time series process, including those exhibiting long-range dependence. Test statistics for composite hypotheses are functionals of a (approximated) martingale transformation of the Bartlett -process with estimated parameters, which converges in distribution to the standard Brownian motion under the null hypothesis. We discuss tests of different natures such as omnibus, directional and Portmanteau-type tests. A Monte Carlo study illustrates the performance of the different tests in practice.
Cite
@article{arxiv.math/0603043,
title = {Distribution free goodness-of-fit tests for linear processes},
author = {Miguel A. Delgado and Javier Hidalgo and Carlos Velasco},
journal= {arXiv preprint arXiv:math/0603043},
year = {2007}
}
Comments
Published at http://dx.doi.org/10.1214/009053605000000606 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)