Rashmi <[email protected]> wants to know how to interpret the -vargranger-
output. Specifically, Rashmi would like to interpret
>. vargranger
> Granger causality Wald tests
> +------------------------------------------------------------------+
> | Equation Excluded | chi2 df Prob > chi2 |
> |--------------------------------------+---------------------------|
> | bp_level lnpetrol | 14.108 4 0.007 |
> | bp_level lndomcred | 6.0917 4 0.192 |
> | bp_level ALL | 19.279 8 0.013 |
> |--------------------------------------+---------------------------|
> | lnpetrol bp_level | 5.9199 4 0.205 |
> | lnpetrol lndomcred | 5.4121 4 0.248 |
> | lnpetrol ALL | 10.121 8 0.257 |
> |--------------------------------------+---------------------------|
> | lndomcred bp_level | 49.78 4 0.000 |
> | lndomcred lnpetrol | 7.3881 4 0.117 |
> | lndomcred ALL | 57.215 8 0.000 |
> +------------------------------------------------------------------+
Variable A is said to Granger cause variable B, if the lags of A can improve
a forecast for variable B. In a VAR model, under the null hypothesis that
variable A does not Granger cause variable B, all the coefficients on the
lags of variable A will be zero in the equation for variable B. A Wald test
is commonly used to test for Granger causality.
Each row of the above table reports a Wald test that the coefficients on the
lags of the variable in the "excluded" column are zero in the equation for
the variable in the "equation" column. For example, the small p-value in
the first row is evidence that the coefficients on the lags of lnpetrol are
not jointly zero in the equation for bp_level, indicating that the evidence
favors the alternative that lnpetrol Granger causes bp_level.
As another example, the second row corresponds to the Wald test that the
coefficients on the lags of lndomcred in the equation for bp_level are
jointly zero. In this case we cannot reject the null hypothesis that
lndomcred does not Granger cause bp_level.
--David --Gustavo
[email protected] [email protected]
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