grangers causation matrix

[mikevogt]

from statsmodels.tsa.stattools import grangercausalitytests

maxlag=12 test = 'ssr_chi2test' def grangers_causation_matrix(data, variables, test='ssr_chi2test', verbose=False):
"""Check Granger Causality of all possible combinations of the Time series. The rows are the response variable, columns are predictors. The values in the table are the P-Values. P-Values lesser than the significance level (0.05), implies the Null Hypothesis that the coefficients of the corresponding past values is zero, that is, the X does not cause Y can be rejected.

data      : pandas dataframe containing the time series variables
variables : list containing names of the time series variables.
"""
df = pd.DataFrame(np.zeros((len(variables), len(variables))), columns=variables, index=variables)
for c in df.columns:
    for r in df.index:
        test_result = grangercausalitytests(data[[r, c]], maxlag=maxlag, verbose=False)
        p_values = [round(test_result[i+1][0][test][1],4) for i in range(maxlag)]
        if verbose: print(f'Y = {r}, X = {c}, P Values = {p_values}')
        min_p_value = np.min(p_values)
        df.loc[r, c] = min_p_value
df.columns = [var + '_x' for var in variables]
df.index = [var + '_y' for var in variables]
return df

grangers_causation_matrix(S_GDO, variables = S_GDO.columns)

[mikevogt]

I managed to fix this issue by adjusting the features we use.