Detecting a structural change in functional time series using local Wilcoxon statistic

Daniel Kosiorowski, Jerzy P. Rydlewski () and Małgorzata Snarska ()
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Daniel Kosiorowski: Cracow University of Economics
Jerzy P. Rydlewski: Faculty of Applied Mathematics
Małgorzata Snarska: Cracow University of Economics

Statistical Papers, 2019, vol. 60, issue 5, No 12, 1677-1698

Abstract: Abstract Functional data analysis is a part of modern multivariate statistics that analyzes data that provide information regarding curves, surfaces, or anything that varies over a certain continuum. In economics and empirical finance, we often have to deal with time series of functional data, where decision cannot be made easily, for example whether they are to be considered as homogeneous or heterogeneous. A discussion on adequate tests of homogenity for functional data is carried out in literature nowadays. We propose a novel statistic for detecting a structural change in functional time series based on a local Wilcoxon statistic induced by a local depth function proposed by Paindaveine and Van Bever, and where a point of the hypothesized structural change is assumed to be known.

Keywords: Functional data analysis; Local depth; Functional depth; Detecting structural change; Heterogenity; Wilcoxon test; 62G30; 62-07; 62G35; 62P20 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00362-017-0891-y

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