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Before testing this effect, it is important to note that the retest finds no evidence of an effect of the index on GDPQ. This is consistent with the previous test in the same data series. Before attempting the Granger causal-lag test, it is necessary to standardize each of these variables that will be tested for this effect, using the VARSTANDARDIZE procedure.
Now that standardization is taken care of, it is time to perform the test. The MIT function COEFF allows the output of the CITISHIFT and CITILAG operations to be saved into two COEFF tables. The coefficients are stored in such a way that the coefficient for the first lag (lag 1) is shown at the left of the coefficient table and the coefficient for the second lag (lag 2) is shown at the top of the table.
In this application, the lag1 coefficient for the t=1 variable EEGP, which is 2.5317, is much greater than 1.997 for the lag1 coefficient for the GDPQ variable, which is -0.2923. In this case, lag1 GDPQ is not Granger-causally related to EEGP. This is supported by a p-value (.9555) that is not significant at the 1 percent level.
This graph provides further evidence of non-causality, that is, the GDPQ variable is not Granger-causing EEGP. The series x1 and x2 provide the observed monthly levels of the two variables. These measures therefore run from January 1980 to December 1991, and include the base year (the value of the pointing variable). There are a total of 2,051 observations.
F-tests involve F- values that are best performed by bootstrapping. That means that you first get data by drawing at random among the original data. Then, after some specified number of samples have been drawn, you abandon drawing and use the remaining data for the F-test. d2c66b5586