Narrowest Significance Pursuit: inference for multiple change-points in linear models

Piotr Fryzlewicz

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We propose Narrowest Significance Pursuit (NSP), a general and flexible methodology for automatically detecting localized regions in data sequences which each must contain a change-point (understood as an abrupt change in the parameters of an underlying linear model), at a prescribed global significance level. NSP works with a wide range of distributional assumptions on the errors, and guarantees important stochastic bounds which directly yield exact desired coverage probabilities, regardless of the form or number of the regressors. In contrast to the widely studied “post-selection inference” approach, NSP paves the way for the concept of “post-inference selection.” An implementation is available in the R package nsp. Supplementary materials for this article are available online.

Keywords: confidence intervals; structural breaks; post-selection inference; wild binary segmentation; narrowest-over-threshold (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2023-06-23
New Economics Papers: this item is included in nep-ecm
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Published in Journal of the American Statistical Association, 23, June, 2023. ISSN: 0162-1459

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:118795

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