Application of Time Series Models in Business Research: Correlation, Association, Causation

Abstract

Time series models are used to determine relationships, spot patterns, and detect abnormalities and irregularities among data. We explore the application of time series analyses in business research by discussing the differences among correlation, association, and Granger causality and providing insight into their proper use in the sustainability literature. In statistics, two correlation coefficients are typically calculated. The first one is the Pearson correlation coefficient and the second is the Spearman correlation coefficient. In the commonly used correlation analysis (the Pearson and the Spearman correlation coefficients), the focus is primarily on the changes in two variables regardless of 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 constant (ceteris paribus). In the study of the causation, or the cause–effect relationship between two variables, researchers are concerned about the effect of variable X on variable Y. The difficulty of achieving the third condition of causation is believed to be the main reason that in business literature causations are rarely used. The difficulty of achieving a causal relationship between two variables has moved researchers toward a special form of causation called “Granger causality”. We offer practical examples for correlation, association, causation, and the Granger causality and discuss their main differences and show how the use of a linear regression is inappropriate when the true relationship is non-linear. Finally, we discuss the policy, practical, and educational implications of our study.