BOOTSTRAP INFERENCE FOR MULTIPLE CHANGE-POINTS IN TIME SERIES

In this paper, we propose two bootstrap procedures, namely parametric and block bootstrap, to approximate the finite sample distribution of change-point estimators for piecewise stationary time series. The bootstrap procedures are then used to develop a generalized likelihood ratio scan method (GLRS...

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Bibliographic Details
Published in:Econometric theory 2022-08, Vol.38 (4), p.752-792
Main Authors: Ng, Wai Leong, Pan, Shenyi, Yau, Chun Yip
Format: Article
Language:English
Subjects:
Approximation
Changes
Econometrics
Economic theory
Genetic algorithms
Random variables
Simulation
Time series
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Summary:In this paper, we propose two bootstrap procedures, namely parametric and block bootstrap, to approximate the finite sample distribution of change-point estimators for piecewise stationary time series. The bootstrap procedures are then used to develop a generalized likelihood ratio scan method (GLRSM) for multiple change-point inference in piecewise stationary time series, which estimates the number and locations of change-points and provides a confidence interval for each change-point. The computational complexity of using GLRSM for multiple change-point detection is as low as $O(n(\log n)^{3})$ for a series of length n. Extensive simulation studies are provided to demonstrate the effectiveness of the proposed methodology under different scenarios. Applications to financial time series are also illustrated.
ISSN:0266-4666
1469-4360
DOI:10.1017/S0266466621000293