Multiple change point detection under serial dependence: wild contrast maximisation and gappy Schwarz algorithm

Haeran Cho and Piotr Fryzlewicz

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

Abstract: We propose a methodology for detecting multiple change points in the mean of an otherwise stationary, autocorrelated, linear time series. It combines solution path generation based on the wild contrast maximisation principle, and an information criterion-based model selection strategy termed gappy Schwarz algorithm. The former is well-suited to separating shifts in the mean from fluctuations due to serial correlations, while the latter simultaneously estimates the dependence structure and the number of change points without performing the difficult task of estimating the level of the noise as quantified e.g. by the long-run variance. We provide modular investigation into their theoretical properties and show that the combined methodology, named WCM.gSa, achieves consistency in estimating both the total number and the locations of the change points. The good performance of WCM.gSa is demonstrated via extensive simulation studies, and we further illustrate its usefulness by applying the methodology to London air quality data.

Keywords: data segmentation; wild binary segmentation; information criterion; autoregressive time series (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2024-05-31
New Economics Papers: this item is included in nep-ecm
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Published in Journal of Time Series Analysis, 31, May, 2024, 45(3), pp. 479 - 494. ISSN: 0143-9782

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