I have been using statsmodels python module to try and learn about Granger Causality. I know that this particular implementation uses four tests for non-causality, but I am having difficulty understanding the output of those tests.
The output is below:
('number of lags (no zero)', 4)
ssr based F test: F=2.5343 , p=0.1677 , df_denom=5 , df_num=4
ssr based chi2 test: chi2=28.3842 , p=0.0000 , df=4
likelihood ratio test: chi2=15.5081 , p=0.0038 , df=4
parameter F test: F=2.5343 , p=0.1677 , df_denom=5 , df_num=4
1) I am looking for a brief explanation of each of the four tests.
2) I am also curious how I should interpret the fact that two of the tests have p-values below 0.05, but two have p-values above 0.05. Does this mean I should reject the null hypothesis, or not?