Some Nonparametric Tests for Comovements between Time Series
时间序列共同变动的若干非参数检验AI 解读 ↓
本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。
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。
时间序列共同变动的若干非参数检验AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
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.