Fast and optimal inference for change points in piecewise polynomials via differencing

Shakeel Gavioli-Akilagun and Piotr Fryzlewicz

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

Abstract: We consider the problem of uncertainty quantification in change point regressions, where the signal can be piecewise polynomial of arbitrary but fixed degree. That is we seek disjoint intervals which, uniformly at a given confidence level, must each contain a change point location. We propose a procedure based on performing local tests at a number of scales and locations on a sparse grid, which adapts to the choice of grid in the sense that by choosing a sparser grid one explicitly pays a lower price for multiple testing. The procedure is fast as its computational complexity is always of the order O(n log(n)) where n is the length of the data, and optimal in the sense that under certain mild conditions every change point is detected with high probability and the widths of the intervals returned match the mini-max localisation rates for the associated change point problem up to log factors. A detailed simulation study shows our procedure is competitive against state of the art algorithms for similar problems. Our procedure is implemented in the R package ChangePointInference which is available via GitHub.

Keywords: confidence intervals; uniform coverage; unconditional coverage; structural breaks; piecewise polynomials; extreme value analysis (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 63 pages
Date: 2025-01-28
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Published in Electronic Journal of Statistics, 28, January, 2025, 19(1), pp. 593 - 655. ISSN: 1935-7524

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