Testing for Serial Correlation in Least Squares Regression. I

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 observa-tions 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. Since the errors in any practical case will be unknown the test must be based on the residuals from the calculated regression. Consequently the ordinary tests of independence