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Bibliographic Details
Main Authors: Barigozzi, Matteo, Cho, Haeran, Trapani, Lorenzo
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2410.02918
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Table of Contents:
  • This paper proposes a moving sum methodology for detecting multiple change points in high-dimensional time series under a factor model, where changes are attributed to those in loadings as well as emergence or disappearance of factors. We establish the asymptotic null distribution of the proposed test for family-wise error control, and show the consistency of the procedure for multiple change point estimation. Simulation studies and an application to a large dataset of volatilities demonstrate the competitive performance of the proposed method.