Moderate deviations for the mildly stationary autoregressive models with dependent errors
Probability
2023-11-08 v2 Statistics Theory
Statistics Theory
Abstract
In this paper, we consider the normalized least squares estimator of the parameter in a mildly stationary first-order autoregressive (AR(1)) model with dependent errors which are modeled as a mildly stationary AR(1) process. By martingale methods, we establish the moderate deviations for the least squares estimators of the regressor and error, which can be applied to understand the near-integrated second order autoregressive processes. As an application, we also obtain the moderate deviations for the Durbin-Watson statistic.
Cite
@article{arxiv.1510.02862,
title = {Moderate deviations for the mildly stationary autoregressive models with dependent errors},
author = {Hui Jiang and Guangyu Yang and Mingming Yu},
journal= {arXiv preprint arXiv:1510.02862},
year = {2023}
}
Comments
31 pages,8 figures, to be published by Statistics