State-domain change point detection for nonlinear time series regression

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

State-domain change point detection for nonlinear time series regression. / Cui, Yan; Yang, Jun; Zhou, Zhou.

In: Journal of Econometrics, Vol. 234, No. 1, 05.2023, p. 3-27.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Cui, Y, Yang, J & Zhou, Z 2023, 'State-domain change point detection for nonlinear time series regression', Journal of Econometrics, vol. 234, no. 1, pp. 3-27. https://doi.org/10.1016/j.jeconom.2021.11.007

APA

Cui, Y., Yang, J., & Zhou, Z. (2023). State-domain change point detection for nonlinear time series regression. Journal of Econometrics, 234(1), 3-27. https://doi.org/10.1016/j.jeconom.2021.11.007

Vancouver

Cui Y, Yang J, Zhou Z. State-domain change point detection for nonlinear time series regression. Journal of Econometrics. 2023 May;234(1):3-27. https://doi.org/10.1016/j.jeconom.2021.11.007

Author

Cui, Yan ; Yang, Jun ; Zhou, Zhou. / State-domain change point detection for nonlinear time series regression. In: Journal of Econometrics. 2023 ; Vol. 234, No. 1. pp. 3-27.

Bibtex

@article{cd0951e8c4dd4dafae95634a31042923,
title = "State-domain change point detection for nonlinear time series regression",
abstract = "Change point detection in time series has attracted substantial interest, but most of the existing results have been focused on detecting change points in the time domain. This paper considers the situation where nonlinear time series have potential change points in the state domain. We apply a density-weighted anti-symmetric kernel function to the state domain and therefore propose a nonparametric procedure to test the existence of change points. When the existence of change points is affirmative, we further introduce an algorithm to estimate the number of change points together with their locations. Theoretical results of the proposed detection and estimation procedures are given and a real dataset is used to illustrate our methods.",
author = "Yan Cui and Jun Yang and Zhou Zhou",
year = "2023",
month = may,
doi = "10.1016/j.jeconom.2021.11.007",
language = "English",
volume = "234",
pages = "3--27",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier",
number = "1",

}

RIS

TY - JOUR

T1 - State-domain change point detection for nonlinear time series regression

AU - Cui, Yan

AU - Yang, Jun

AU - Zhou, Zhou

PY - 2023/5

Y1 - 2023/5

N2 - Change point detection in time series has attracted substantial interest, but most of the existing results have been focused on detecting change points in the time domain. This paper considers the situation where nonlinear time series have potential change points in the state domain. We apply a density-weighted anti-symmetric kernel function to the state domain and therefore propose a nonparametric procedure to test the existence of change points. When the existence of change points is affirmative, we further introduce an algorithm to estimate the number of change points together with their locations. Theoretical results of the proposed detection and estimation procedures are given and a real dataset is used to illustrate our methods.

AB - Change point detection in time series has attracted substantial interest, but most of the existing results have been focused on detecting change points in the time domain. This paper considers the situation where nonlinear time series have potential change points in the state domain. We apply a density-weighted anti-symmetric kernel function to the state domain and therefore propose a nonparametric procedure to test the existence of change points. When the existence of change points is affirmative, we further introduce an algorithm to estimate the number of change points together with their locations. Theoretical results of the proposed detection and estimation procedures are given and a real dataset is used to illustrate our methods.

U2 - 10.1016/j.jeconom.2021.11.007

DO - 10.1016/j.jeconom.2021.11.007

M3 - Journal article

VL - 234

SP - 3

EP - 27

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 1

ER -

ID: 361385224