Bootstrap Inference for Multiple Change-points in Time Series
【摘要】：In this paper we propose two bootstrap procedures, namely parametric and block bootstrap, to approximate the asymptotic distribution of change-point estimator for piecewise stationary time series. The bootstrap procedures are then used to develop a generalized likelihood ratio scan method(GLRSM) for multiple change-points inference in piecewise stationary time series, which estimates the number and positions of change-points and provides confidence interval for each change-point. The computational complexity of using GLRSM for multiple change-points 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. Research supported in part by HKSAR-RGC Grants.