| A NEW TEST FOR NORMALITY IN LINEAR AUTOREGRESSIVE MODELS |
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| 作者姓名： | CHEN Min WU Guofu Gemai |
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| 作者单位： | CHEN Min WU Guofu (Institute of Applied Mathematics，Chinese Academy of Sciences，Beijing 100080，China)Gemai Chen(University of Calgary，Calgary，Alberta，T2N 1N4，Canada) |
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| 基金项目： | This research is supported by the National Natural Science Foundation of China(No.19971093)，the Knowledge Innovation Program of the Chinese Academy of Sciences (No. KZCX2-SW-118). |
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| 摘 要： | A nonparametric test for normality of linear autoregressive time series is proposed in this paper. The test is based on the best one-step forecast in mean square with time reverse. Some asymptotic theory is developed for the test, and it is shown that the test is easy to use and has good powers. The empirical percentage points to conduct the test in practice are provided and three examples using real data are included.
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| 关 键 词： | 非参数检定 线性自回归时间序列 预测 线性自回归模型 |
A NEW TEST FOR NORMALITY IN LINEAR AUTOREGRESSIVE MODELS |
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| CHEN Min WU Guofu Gemai.A NEW TEST FOR NORMALITY IN LINEAR AUTOREGRESSIVE MODELS[J].Journal of Systems Science and Complexity,2002,15(4):423-435. |
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| Authors: | CHEN Min WU Guofu |
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| Abstract: | A nonparametric test for normality of linear autoregressive time series is proposed in this paper. The test is based on the best one-step forecast in mean square with time reverse. Some asymptotic theory is developed for the test, and it is shown that the test is easy to use and has good powers. The empirical percentage points to conduct the test in practice are provided and three examples using real data are included. |
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| Keywords: | Nonparametric test time-reversibility one-step forecast Kolmogorov-Smirnov statistic |
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