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Abstract

This article discusses the effect of an autocorrelated error structure on the interpretation of traditional significance tests, especially the t-test and R2 measure It emphasizes first-order serial correlation, a common and often serious problem that researchers using time series data may encounter Even though many of the problems associated with an autocorrelated error structure are well known, many researchers ignore them and report results which range from being potentially misleading to grossly erroneous

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