VARResults.test_causality()

statsmodels.tsa.vector_ar.var_model.VARResults.test_causality

VARResults.test_causality(equation, variables, kind='f', signif=0.05, verbose=True) [source]

Compute test statistic for null hypothesis of Granger-noncausality, general function to test joint Granger-causality of multiple variables

Parameters:	equation : string or int Equation to test for causality variables : sequence (of strings or ints) List, tuple, etc. of variables to test for Granger-causality kind : {‘f’, ‘wald’} Perform F-test or Wald (chi-sq) test signif : float, default 5% Significance level for computing critical values for test, defaulting to standard 0.95 level
Returns:	results : dict

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.