August 2024 Detecting long-range dependence for time-varying linear models
Lujia Bai, Weichi Wu
Author Affiliations +
Bernoulli 30(3): 2450-2474 (August 2024). DOI: 10.3150/23-BEJ1680

Abstract

We consider the problem of testing for long-range dependence in time-varying coefficient regression models, where the covariates and errors are locally stationary, allowing complex temporal dynamics and heteroscedasticity. We develop KPSS, R/S, V/S, and K/S-type statistics based on the nonparametric residuals. Under the null hypothesis, the local alternatives as well as the fixed alternatives, we derive the limiting distributions of the test statistics. As the four types of test statistics could degenerate when the time-varying mean, variance, long-run variance of errors, covariates, and the intercept lie in certain hyperplanes, we show the bootstrap-assisted tests are consistent under both degenerate and non-degenerate scenarios. In particular, in the presence of covariates the exact local asymptotic power of the bootstrap-assisted tests can enjoy the same order as that of the classical KPSS test of long memory for strictly stationary series. The asymptotic theory is built on a new Gaussian approximation technique for locally stationary long-memory processes with short-memory covariates, which is of independent interest. The effectiveness of our tests is demonstrated by extensive simulation studies and real data analysis.

Funding Statement

Weichi Wu is the corresponding author and gratefully acknowledges “NSFC Program (no.12271287), (no. 11901337) and BJNSF (no.Z190001) of China”.

Acknowledgments

The authors would like to thank the editor, the associate editor and two anonymous reviewers for their helpful comments.

Citation

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Lujia Bai. Weichi Wu. "Detecting long-range dependence for time-varying linear models." Bernoulli 30 (3) 2450 - 2474, August 2024. https://doi.org/10.3150/23-BEJ1680

Information

Received: 1 January 2023; Published: August 2024
First available in Project Euclid: 14 May 2024

Digital Object Identifier: 10.3150/23-BEJ1680

Keywords: locally stationary process , long-range dependence , spurious long memory , time-varying models

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Vol.30 • No. 3 • August 2024
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