Monitoring Distributional Changes in Autoregressive Models Based on Weighted Empirical Process of Residuals
Received:December 10, 2013  Revised:March 09, 2015
Key Words: distributional changes   autoregressive models   weighted empirical process of residuals  
Fund Project:Supported by the National Natural Science Foundation of China (Grant No.11301291) and the Open Fund of State Key Laboratory of Remote Sensing Science of China (Grant No.OFSLRSS201206).
Author NameAffiliation
Fuxiao LI Department of Applied Mathematics, Northwestern Polytechnical University, Shaanxi 710129, P. R. China 
Zheng TIAN Department of Applied Mathematics, Northwestern Polytechnical University, Shaanxi 710129, P. R. China
State Key Laboratory of Remote Sensing Science, Chinese Academy of Science, Beijing 100101, P. R. China 
Zhanshou CHEN Department of Mathematics and information, Qinghai Normal University, Qinghai 810008, P. R. China 
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Abstract:
      Change monitoring of distribution in time series models is an important issue. This paper proposes a procedure for monitoring changes in the error distribution of autoregressive time series, which is based on a weighed empirical process of residuals with weights equal to the regressors. The asymptotic properties of our monitoring statistic are derived under the null hypothesis of no change in distribution. The finite sample properties are investigated by a simulation. As it turns out, the procedure is not only able to detect distributional changes but also changes in the regression coefficient and mean. Finally, we apply the statistic to a groups of financial data.
Citation:
DOI:10.3770/j.issn:2095-2651.2015.03.011
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