JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2022, Vol. 57 ›› Issue (4): 91-99.doi: 10.6040/j.issn.1671-9352.0.2021.525

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Online monitoring of parameter changes in linear regression model with long memory errors

NIANG Mao-cuo, CHEN Zhan-shou*, CHENG Shou-yao, WANG Xiao-yang   

  1. School of Mathematics and Statistics, Qinghai Normal University, Xining 810008, Qinghai, China
  • Online:2022-04-20 Published:2022-03-29

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Abstract: Based on the modified moving-sum-statistic(mMOSUM)method, this paper studies the online monitoring change points of the regression coefficients of linear regression model with long-memory time series errors. Under the null hypothesis, the limit distribution of the mMOSUM monitoring statistics is obtained by modifying the boundary function, and the consistency of the method is proved under the alternative hypothesis. The results of numerical simulation show that when linear regression model has long memory errors, the mMOSUM method is still effective except for the case where the long memory parameter value is larger. And the location of change point moves further back, the effect of modified method on the increase of the power and the reduction of the run length is more obvious. Finally, the feasibility of this method is demonstrated by an empirical analysis of a set of macroeconomic data for the United States.

Key words: linear regression model, long memory time series, change point monitoring, modified moving-sum-statistic(mMOSUM)method

CLC Number: 

  • O212.1
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