Article ID Journal Published Year Pages File Type
1150522 Journal of Statistical Planning and Inference 2008 25 Pages PDF
Abstract

We study an autoregressive time series model with a possible change in the regression parameters. Approximations to the critical values for change-point tests are obtained through various bootstrapping methods. Theoretical results show that the bootstrapping procedures have the same limiting behavior as their asymptotic counterparts discussed in Hušková et al. [2007. On the detection of changes in autoregressive time series, I. Asymptotics. J. Statist. Plann. Inference 137, 1243–1259]. In fact, a small simulation study illustrates that the bootstrap tests behave better than the original asymptotic tests if performance is measured by the αα- and ββ-errors, respectively.

Related Topics
Physical Sciences and Engineering Mathematics Applied Mathematics
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