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分类依据与纠错

AI 根据所提供的论文材料归类:摘要直接研究时间序列共同变动检验,并将趋势和差分符号的序列相关作为核心处理对象。

反馈分类问题

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代码、数据与补充材料

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关注这篇论文的作者
Leo A. Goodman
Yehuda Grunfeld

时间序列共同变动的若干非参数检验

研究问题
检验两条时间序列的变动是否相关时,如何同时处理趋势影响和一阶差分符号自身的序列相关问题?
方法与思路
作者修改 Moore–Wallis 检验,使其考虑纯随机过程差分符号的负序列相关,并讨论较一般时间序列及多序列情形。
材料陈述的贡献
摘要提出简单的检验修正,并用两个数值例子说明趋势与符号序列相关的影响;另简要讨论三条以上序列的共同变动。
阅读关注点
应核对修正统计量、零假设及适用过程类别,明确趋势如何处理、符号相关如何进入校正,以及多序列讨论的完整程度。
阅读边界
仅据 OpenAlex 索引摘要,稿件版本未注明,不能判断刊本变化;摘要未给出检验分布、显著性水平保证或功效比较。

原文摘要

The test, proposed by Moore and Wallis [23], of the existence of correlation between the movements in two time series (eliminating at least the primary effects of trends) was justified by them in “the special case of randomly arranged [signs of the] first differences” in both series. Moore and Wallis also noted that the signs of the first differences in a time series will have a negative serial correlation if the time series is a purely random process (or if it is a particular kind of generalization of a purely random process). It is therefore necessary to modify the Moore-Wallis test when applying it to such time series in order to take into account this serial correlation. In the present article, a simple modified form of the Moore-Wallis test is suggested, which can be applied to such time series. Somewhat similar modifications are also suggested for testing the existence of correlation between some more general kinds of time series. The importance of taking into account the effects of trends in the time series and the effects of the serial correlations (between the signs of the first differences) is illustrated by means of two numerical examples. Finally, the study of the movements in three or more time series is briefly discussed.

笔记与分组
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