Robust Narrowest Significance Pursuit: inference for multiple change-points in the median

Piotr Fryzlewicz

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

Abstract: We propose Robust Narrowest Significance Pursuit (RNSP), a methodology for detecting localized regions in data sequences which each must contain a change-point in the median, at a prescribed global significance level. RNSP works by fitting the postulated constant model over many regions of the data using a new sign-multiresolution sup-norm-type loss, and greedily identifying the shortest intervals on which the constancy is significantly violated. By working with the signs of the data around fitted model candidates, RNSP fulfils its coverage promises under minimal assumptions, requiring only sign-symmetry and serial independence of the signs of the true residuals. In particular, it permits their heterogeneity and arbitrarily heavy tails. The intervals of significance returned by RNSP have a finite-sample character, are unconditional in nature and do not rely on any assumptions on the true signal. Code implementing RNSP is available at https://github.com/pfryz/nsp.

Keywords: confidence intervals; structural breaks; post-selection inference; post-inference selection; narrowest-over-threshold; Confidence intervals (search for similar items in EconPapers)
JEL-codes: C1 J1 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2024-10-31
New Economics Papers: this item is included in nep-ecm and nep-inv
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Published in Journal of Business and Economic Statistics, 31, October, 2024, 42(4), pp. 1389-1402. ISSN: 0735-0015

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