Testing for autocorrelation in linear regression models: a survey

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Abstract

The seminal work of Cochrane and Orcutt (1949) did much to alert econometricians to the difficulties of assuming uncorrelated disturbances in time series applications of the general linear model. Because the neglect of correlation between regression disturbances can lead to inefficient parameter estimates, misleading inferences from hypothesis tests and inefficient predictions, the desirability of testing for the presence of such correlation is widely accepted. This paper attempts to survey the vast and varied literature concerned with testing for autocorrelation in the context of the linear regression model. This particular testing problem must surely be the most intensely researched testing problem in econometrics. We will therefore be interested to see what lessons concerning hypothesis testing in econometrics can be learnt from the study of this literature.

Original languageEnglish
Title of host publicationSpecification Analysis in the Linear Model
EditorsMaxwell L. King, David E. A. Giles
Place of PublicationLondon UK
PublisherRoutledge
Chapter3
Pages19-73
Number of pages55
Edition1st
ISBN (Electronic)9781351140676, 9781351140683
ISBN (Print)0710206143, 9780815350545
DOIs
Publication statusPublished - 1987

Publication series

NameRoutledge Library Editions: Econometrics
PublisherRoutledge
Volume11

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