Bootstrap confidence intervals for multiple change points based on moving sum procedures

Haeran Cho and Claudia Kirch

Computational Statistics & Data Analysis, 2022, vol. 175, issue C

Abstract: The problem of quantifying uncertainty about the locations of multiple change points by means of confidence intervals is addressed. The asymptotic distribution of the change point estimators obtained as the local maximisers of moving sum statistics is derived, where the limit distributions differ depending on whether the corresponding size of changes is local, i.e. tends to zero as the sample size increases, or fixed. A bootstrap procedure for confidence interval generation is proposed which adapts to the unknown magnitude of changes and guarantees asymptotic validity both for local and fixed changes. Simulation studies show good performance of the proposed bootstrap procedure, and some discussions about how it can be extended to serially dependent errors are provided.

Keywords: Data segmentation; Change point estimation; Efron's bootstrap; Moving sum statistics; Scan statistics (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:175:y:2022:i:c:s0167947322001323

DOI: 10.1016/j.csda.2022.107552

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