Bootstrapping confidence intervals for the change‐point of time series

By Marie Husková, Claudia Kirch. 2007

61 citations recorded by the source corpus.

Abstract.  We study an at‐most‐one‐change time‐series model with an abrupt change in the mean and dependent errors that fulfil certain mixing conditions. We obtain confidence intervals for the unknown change‐point via bootstrapping methods. Precisely, we use a block bootstrap of the estimated centred error sequence. Then, we reconstruct a sequence with a change in the mean using the same estimators as before. The difference between the change‐point estimator of the resampled sequence and the one of the original sequence can be used as an approximation of the difference between the real change‐point and its estimator. This enables us to construct confidence intervals using the empirical distribution of the resampled time series. A simulation study shows that the resampled confidence intervals are usually closer to their target levels and at the same time smaller than the asymptotic intervals.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3397cfa8c02384f3c822e9eaa43abb2b7710adf5

Citations in the corpus

• Inference for single and multiple change‐points in time series (2013, unknown): “2013, 34 423–446 1997b, 1998, 2000, 2010), Bai and Perron (1998, 2003), Bai et al. (1998), Huskova (1996), Huskova and Kirch (2008), Raimondo (1998), Fiteni (2002), Guan (2004), Zou et al. (2007) and Hariz et al. (2007).”