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Modified procedures for change point monitoring in linear models

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  • Chen, Zhanshou
  • Tian, Zheng

Abstract

Monitoring on-line data to detect change point as early as possible is an important issue. It is shown that the existing CUSUM test is inefficient to quickly give an alarm when change point does not occur at the early stage of monitoring. In this paper we propose a set of new monitoring procedures to detect coefficients and error variance change in linear regression models. Our proposed modification, which uses a bandwidth parameter to change the beginning time of monitoring, can detect change point more quickly even if it occurs after a relative longer monitoring time. Simulations suggest that the modified procedures compared with the CUSUM test have the same null distribution but higher power and shorter average run length. In particular, we illustrate the effectiveness of our procedures by IBM stock data and Thailand/U.S. foreign exchange rate data.

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  • Chen, Zhanshou & Tian, Zheng, 2010. "Modified procedures for change point monitoring in linear models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(1), pages 62-75.
    • Handle: RePEc:eee:matcom:v:81:y:2010:i:1:p:62-75
    DOI: 10.1016/j.matcom.2010.06.021
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    References listed on IDEAS

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    1. Sangyeol Lee & Siyun Park, 2001. "The Cusum of Squares Test for Scale Changes in Infinite Order Moving Average Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(4), pages 625-644, December.
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    Cited by:

    1. Zhanshou Chen & Yanting Xiao & Fuxiao Li, 2021. "Monitoring memory parameter change-points in long-memory time series," Empirical Economics, Springer, vol. 60(5), pages 2365-2389, May.
    2. Claudia Kirch & Christina Stoehr, 2022. "Sequential change point tests based on U‐statistics," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1184-1214, September.
    3. Josua Gösmann & Tobias Kley & Holger Dette, 2021. "A new approach for open‐end sequential change point monitoring," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(1), pages 63-84, January.

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