Test of change point versus long‐range dependence in functional time series

Author:

Baek Changryong1,Kokoszka Piotr2ORCID,Meng Xiangdong2

Affiliation:

1. Sungkyunkwan University Seoul South Korea

2. Colorado State University Fort Collins CO USA

Abstract

In the context of functional time series, we propose a significance test to distinguish between short memory with a change point and long range dependence. The test is based on coefficients of projections onto an optimal direction that captures the dependence structure of the latent stationary functions that are not observable due to a potential change point. The optimal direction must be estimated as well. The test statistic is constructed using the local Whittle estimator applied to these coefficients. It has standard normal distribution under the null hypothesis (change point) and diverges to infinity under the alternative (long range dependence). The article includes asymptotic theory, a simulation study and an application to curve‐valued time series derived from intraday asset prices.

Funder

National Science Foundation

National Research Foundation of Korea

Publisher

Wiley

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability

Reference27 articles.

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2. Evaluating stationarity via change–point alternatives with applications to fMRI data;Aston JAD;The Annals of Applied Statistics,2012

3. Estimation of a change–point in the mean function of functional data;Aue A;Journal of Multivariate Analysis,2009

4. Detecting and dating structural breaks in functional data without dimension reduction;Aue A;Journal of the Royal Statistical Society (B),2018

5. Statistical tests for changes in mean against long‐range dependence;Baek C;Journal of Time Series Analysis,2012

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1. Test for the mean of high-dimensional functional time series;Computational Statistics & Data Analysis;2025-01

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