Estimation and Inference of Time-Varying Auto-Covariance under Complex Trend: A Difference-based Approach
Statistics Theory
2020-03-12 v1 Statistics Theory
Abstract
We propose a difference-based nonparametric methodology for the estimation and inference of the time-varying auto-covariance functions of a locally stationary time series when it is contaminated by a complex trend with both abrupt and smooth changes. Simultaneous confidence bands (SCB) with asymptotically correct coverage probabilities are constructed for the auto-covariance functions under complex trend. A simulation-assisted bootstrapping method is proposed for the practical construction of the SCB. Detailed simulation and a real data example round out our presentation.
Cite
@article{arxiv.2003.05006,
title = {Estimation and Inference of Time-Varying Auto-Covariance under Complex Trend: A Difference-based Approach},
author = {Yan Cui and Michael Levine and Zhou Zhou},
journal= {arXiv preprint arXiv:2003.05006},
year = {2020}
}