# statsmodels.tsa.vector_ar.var_model.VARResults.test_causality¶

method

VARResults.test_causality(caused, causing=None, kind='f', signif=0.05)[source]

Test Granger causality

Parameters
causedint or str or sequence of int or str

If int or str, test whether the variable specified via this index (int) or name (str) is Granger-caused by the variable(s) specified by causing. If a sequence of int or str, test whether the corresponding variables are Granger-caused by the variable(s) specified by causing.

causingint or str or sequence of int or str or None, default: None

If int or str, test whether the variable specified via this index (int) or name (str) is Granger-causing the variable(s) specified by caused. If a sequence of int or str, test whether the corresponding variables are Granger-causing the variable(s) specified by caused. If None, causing is assumed to be the complement of caused.

kind{‘f’, ‘wald’}

Perform F-test or Wald (chi-sq) test

signiffloat, default 5%

Significance level for computing critical values for test, defaulting to standard 0.05 level

Returns
resultsCausalityTestResults

Notes

Null hypothesis is that there is no Granger-causality for the indicated variables. The degrees of freedom in the F-test are based on the number of variables in the VAR system, that is, degrees of freedom are equal to the number of equations in the VAR times degree of freedom of a single equation.

Test for Granger-causality as described in chapter 7.6.3 of [1]. Test H0: “causing does not Granger-cause the remaining variables of the system” against H1: “causing is Granger-causal for the remaining variables”.

References

1(1,2)

Lütkepohl, H. 2005. New Introduction to Multiple Time Series Analysis. Springer.