So how can you explain causality? A statement such as *“J causes Q”* will have the following meaning in different scenarios and disciplines such as *J* leads *Q, J* is the only cause of *Q, J* is only one of the possible causes of *Q, J* must always lead to *Q* (that is, *J* determines *Q*), the occurrence of *J* makes the occurrence of *Q * more probable, *J* is a probabilistic cause of *Q, J* must occur either before or simultaneously with *Q*, but not afterwards, past values of *J* forecasts future values of *Q*.

But Regression analysis deals with the dependence of one variable on other variables, it does not necessarily imply causation. In other words, the existence of a relationship between variables does not prove causality or the direction of influence. But in regressions involving time series data, the situation may be somewhat different.

Short-run causal effects: through the *F*-statistics and the statistical significance of the regressors.

Long-run causal effects: through the statistical significance error-correction term (applicable to VECM only).

Joint causal effects: through the *F*-statistics and the significance of the independent variables and the statistical significance error-correction term (applicable to VECM only).

Unidirectional causality: occurs from *J* to *Q* if the set of estimated coefficients of the lagged *J* are significantly different from zero and the set of estimated coefficients of lagged *Q* are *not* significantly different from zero.

Bi-directional causality: occurs from *J* to *Q* if the set of estimated coefficients of the lagged *J* are significantly different from zero and vice-versa.

Independence: occurs from *Q* (*J*) to *J* (*Q*) if the set of estimated coefficients of the lagged *Q (J)* are not significantly different from zero. Using EViews10, this video shows you how to perform causality tests in four different ways within a VAR framework and interpret the results.