Work

Moving Sum Procedure for Multiple Change Point Detection in Large Factor Models

Year: 2025

Type: article

Abstract: ABSTRACT 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.

Source: Journal of Time Series Analysis

DOI: 10.1111/jtsa.70028

Authors: Matteo Barigozzi, Haeran Cho, Lorenzo Trapani

Institutions: University of Bologna, University of Bristol, University of Leicester, University of Pavia

Language: English

Topic: Advanced Statistical Process Monitoring

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,510

Funder: Engineering and Physical Sciences Research Council