Testing for changes in linear models using weighted residuals

Lajos Horvath, Gregory Rice and Yuqian Zhao

Journal of Multivariate Analysis, 2023, vol. 198, issue C

Abstract: We study methods for detecting change points in linear regression models. Motivated by statistics arising from maximally selected likelihood ratio tests, we provide an asymptotic theory for weighted functionals of the cumulative sum (CUSUM) process of linear model residuals. Special attention is given to standardized quadratic form statistics, leading to Darling–Erdős type limit results, as well as novel heavily weighted CUSUM statistics that increase the power of the tests to detect changes that occur early or late in the sample. We discuss improved finite-sample approaches to estimate the critical values for the proposed statistics, which are shown to work well in a Monte Carlo simulation study. The proposed tests are applied to the environmental Kuznets curve, and a COVID-19 dataset.

Keywords: Change point detection; Linear regression; Weighted cumulative sums (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X23000568
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:198:y:2023:i:c:s0047259x23000568

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2023.105210

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().