Correlation, association, causation, and Granger causation in accounting research

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In this paper we discuss the differences between correlation, association, and Granger causation. We argue that these important topics are not used properly in accounting and auditing. In statistics two correlation coefficients are calculated. The first one is the Pearson correlation coefficient and the other one is the Spearman correlation coefficient. In correlation analysis, the focus is only on the changes in two variables and no effort is made to control the effects of other variables. On the contrary, in association analyses the researcher examines the relationship between two variables while holding the effects of other related variables unchanged (ceteris paribus). In study of the causation or the cause-effect relationship between two variables, researchers are concerned about the effect of X on Y. The difficulty of achieving the third condition of causation is probably the main reason that in accounting literature the causation or cause-effect relationships are rarely used. The difficulty of achieving a causal relationship between two variables moved researchers toward a special form of causation called "Granger Causation". We have provided practical examples for correlation, association, causation, and the Granger causation and discuss their main differences. By providing empirical examples, we also show how the use of a linear regression is not appropriate when the true relationship is not linear. Finally, we have discussed the policy, practical, and educational Implications of our study.

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Academy of Accounting and Financial Studies Journal

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