A characterization of ARMA and Fractional ARIMA models with infinitely divisible innovations
Statistics Theory
2019-05-23 v3 Statistics Theory
Abstract
The object of this paper is to study the asymptotic dependence structure of the linear time series models with infinitely divisible innovations by the use of their characteristic functions. Autoregressive moving-average (ARMA) models and fractional autoregressive integrated moving-average (FARIMA) models are analyzed. As examples of infinitely divisible innovations, the class of radially absolute continuous distributions and general non-symmetric stable distributions are considered. The finite dimensional distributionsn of these models are also obtained.
Keywords
Cite
@article{arxiv.math/0703731,
title = {A characterization of ARMA and Fractional ARIMA models with infinitely divisible innovations},
author = {Muneya Matsui},
journal= {arXiv preprint arXiv:math/0703731},
year = {2019}
}
Comments
This paper has been withdrawn by the author since the author thought there is no actual contribution