The Granger (1969) approach to the question of whether x causes y is to see how much of the current y can be explained by past values of y and then to see whether adding lagged values of x can improve the explanation. y is said to be Granger-caused by x if x helps in the prediction of , or equivalently if the coefficients on the lagged x’s are statistically significant. Note that two-way causation is frequently the case; x Granger causes y and Granger causes x.
It is important to note that the statement “x Granger causes y” does not imply that is the effect or the result of x. Granger causality measures precedence and information content but does not by itself indicate causality in the more common use of the term.
When you select the Granger Causality view, you will first see a dialog box asking for the number of lags to use in the test regressions. In general, it is better to use more rather than fewer lags, since the theory is couched in terms of the relevance of all past information. You should pick a lag length, , that corresponds to reasonable beliefs about the longest time over which one of the variables could help predict the other.
EViews runs bivariate regressions of the form:
y(t)=a(0)+a(1)y(t-1)+...+a(l)y(t-l)+b(1)x(t-1)+...+b(l)x(t-l)+e(t)
x(t)=a(0)+a(1)x(t-1)+...+a(l)x(t-l)+b(1)y(t-1)+...+b(l)y(t-l)+e(t)
for all possible pairs of (x, y) series in the group. The reported F-statistics are the Wald statistics for the joint hypothesis:
b(1)=b(2)=...=b(l)=0
for each equation. The null hypothesis is that x does not Granger-cause y in the first regression and that y does not Granger-cause x in the second regression.
Granger causality test:
Performs pairwise Granger causality tests between (all possible) pairs of the listed series or group of series.
Syntax
Command: cause(n, options) ser1 ser2 ser3
Group View: group_name.cause(n, options)
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