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Change detection in autoregressive time series

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  • Gombay, Edit

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.

Suggested Citation

  • Gombay, Edit, 2008. "Change detection in autoregressive time series," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 451-464, March.
  • Handle: RePEc:eee:jmvana:v:99:y:2008:i:3:p:451-464
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    Citations

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    Cited by:

    1. Jirak, Moritz, 2012. "Change-point analysis in increasing dimension," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 136-159.
    2. 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.
    3. Pouliot, William, 2016. "Robust tests for change in intercept and slope in linear regression models with application to manager performance in the mutual fund industry," Economic Modelling, Elsevier, vol. 58(C), pages 523-534.
    4. Pap Gyula & Szabó Tamás T., 2016. "Change detection in the Cox–Ingersoll–Ross model," Statistics & Risk Modeling, De Gruyter, vol. 33(1-2), pages 21-40, September.
    5. repec:eee:csdana:v:121:y:2018:i:c:p:41-56 is not listed on IDEAS
    6. Gombay, Edit & Serban, Daniel, 2009. "Monitoring parameter change in time series models," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 715-725, April.

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