Granger causality analysis is a statistical method to investigate causality between two variables in a time series, like whether a set of time series X is the cause of another set of time series Y. It is based on an autoregressive model in regression analysis. Regression analysis usually only yields the contemporaneous correlation between different variables; the autoregressive model can only obtain the correlation between the previous and the second variables; but the Nobel Prize winner in economics, Clive Granger found that it is feasible to reveal the time-difference correlation between different variables through a series of tests in the autoregressive model.

The conclusion of the Granger causality test is statistical causality, not necessarily causality in the true sense of the word. But because the causal relationship in the statistical sense is also meaningful, it has certain reference value.

When the P-value is less than 0.05, the null hypothesis is rejected, and vice versa.

Drag one variable in the “Dimension” field, then two variables in the “Value” field, and click Granger causality in the Statistical Module to calculate the causality between variables.