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
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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 Name | Affiliation | 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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