TESTING FOR SERIAL CORRELATION IN LEAST SQUARES REGRESSION .2.
TESTING FOR SERIAL CORRELATION IN LEAST SQUARES REGRESSION .2.
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DOI：
10.1093/biomet/38.1-2.159
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发表时间：
1951-01-01
期刊：
影响因子：
2.7
通讯作者：
WATSON, GS
中科院分区：
文献类型：
--
作者：
DURBIN, J;WATSON, GS
毫无疑问，在已知最小二乘回归方法不适用的情况下，它们被大量使用。特别是，它们经常被用于分析时间序列和类似的数据，在这些数据中，连续的观测数据是序列相关的。由此产生的并发症是众所周知的，最近Cochrane&Orutt(1949)从计量经济学的角度对其进行了研究。应用最小二乘法的一个基本假设是回归模型中的误差项是独立的。当这一假设--以及其他假设--成立时，无论观测数据本身是否具有序列相关性，该过程都是有效的。检验误差是否独立的问题构成了本文及其后继者的主题。本文主要讨论测试所依据的理论，而第二篇文章详细描述了测试程序，并给出了所采用的测试标准的有效点的界限表。在这两篇论文中，我们都不会关心如果测试结果不好应该做些什么的问题。
A great deal of use has undoubtedly been made of least squares regression methods in circumstances in which they are known to be inapplicable. In particular, they have often been employed for the analysis of time series and similar data in which successive observations are serially correlated. The resulting complications are well known and have recently been studied from the standpoint of the econometrician by Cochrane & Orcutt (1949). A basic assumption underlying the application of the least squares method is that the error terms in the regression model are independent. When this assumption—among others—is satisfied the procedure is valid whether or not the observations themselves are serially correlated. The problem of testing the errors for independence forms the subject of this paper and its successor. The present paper deals mainly with the theory on which the test is based, while the second paper describes the test procedures in detail and gives tables of bounds to the significance points of the test criterion adopted. We shall not be concerned in either paper with the question of what should be done if the test gives an unfavourable result.