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Change detection in autoregressive time series

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  • Gombay, Edit
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    Abstract

    Autoregressive time series models of order p have p+2 parameters, the mean, the variance of the white noise and the p autoregressive parameters. Change in any of these over time is a sign of disturbance that is important to detect. The methods of this paper can test for change in any one of these p+2 parameters separately, or in any collection of them. They are available in forms that make one-sided tests possible, furthermore, they can be used to test for a temporary change. The test statistics are based on the efficient score vector. The large sample properties of the change-point estimator are also explored.

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    File URL: http://www.sciencedirect.com/science/article/B6WK9-4MTC6FP-2/2/5d8e9f76b362d35e233ef8ea7cd9256d
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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 99 (2008)
    Issue (Month): 3 (March)
    Pages: 451-464

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    Handle: RePEc:eee:jmvana:v:99:y:2008:i:3:p:451-464

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    Related research

    Keywords: Time series Efficient score vector Strong approximation Invariance Principles Brownian bridge;

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    Cited by:
    1. Jin, Hao & Zhang, Jinsuo, 2010. "Subsampling tests for variance changes in the presence of autoregressive parameter shifts," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2255-2265, November.

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