Granger Causality Test: A Useful Descriptive Tool for Time Series Data

Abstract

(Department of Cyber-Security Science, Federal University of Technology, Minna) Interdependency of one or more variables on the other has been in the existence over long time when it was discovered that one variable has to move or regress toward another following the work done by Galton (1886); Pearson & Lee (1903); Kendall & Stuart, (1961); Johnston and DiNardo, (1997); Gujarati, (2004) etc. It was in the light of this dependency over time the researcher uses Granger Causality as an effective tool in time series Predictive causality using Nigeria GDP and Money Supply to know the type of causality in existence in the two time series variables under consideration and which one can statistically predicts the other. The research work aimed at testing for nature of causality between GDP and money supply for Federal Republic of Nigeria for the period of thirty years using the data sourced from Central Bank of Nigeria Statistical Bulletin. After observing the various conditions of Granger causality test such as ensuring stationarity in the variables under consideration; adding enough number of lags in the prescribed model before estimation as Granger causality test is sensitive to the number of lags introduced in the model; and as well as assuming the disturbance terms in the various models are uncorrelated, the result of the analysis indicates a bilateral relationship between Nigeria GDP and Money Supply. It implies Nigeria GDP Granger causes money Supply and vice versa. Based on the result of this study, both Nigeria GDP and money Supply can be successfully model using Vector Autoregressive Model since changes in one variable has a significant effect on the other variable