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.

Here is the link to the ex21-1.wf1 dataset (EViews file) used for this tutorial (endeavour to have a Google account for easy accessibility): [ Ссылка ]

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