statsmodels.tsa.vector_ar.svar_model.SVARResults.test_inst_causality¶
-
SVARResults.test_inst_causality(causing, signif=
0.05)¶ Test for instantaneous causality
- Parameters:¶
- causing
If int or str, test whether the corresponding variable is causing the variable(s) specified in caused. If sequence of int or str, test whether the corresponding variables are causing the variable(s) specified in caused.
- signif
floatbetween0and1,default5 % Significance level for computing critical values for test, defaulting to standard 0.05 level
- verbosebool
If True, print a table with the results.
- Returns:¶
- results
dict A dict holding the test’s results. The dict’s keys are:
- “statistic”float
The calculated test statistic.
- “crit_value”float
The critical value of the Chi^2-distribution.
- “pvalue”float
The p-value corresponding to the test statistic.
- “df”float
The degrees of freedom of the Chi^2-distribution.
- “conclusion”str {“reject”, “fail to reject”}
Whether H0 can be rejected or not.
- “signif”float
Significance level
- results
Notes
Test for instantaneous causality as described in chapters 3.6.3 and 7.6.4 of [1]. Test H0: “No instantaneous causality between caused and causing” against H1: “Instantaneous causality between caused and causing exists”.
Instantaneous causality is a symmetric relation (i.e. if causing is “instantaneously causing” caused, then also caused is “instantaneously causing” causing), thus the naming of the parameters (which is chosen to be in accordance with test_granger_causality()) may be misleading.
This method is not returning the same result as JMulTi. This is because the test is based on a VAR(k_ar) model in statsmodels (in accordance to pp. 104, 320-321 in [1]) whereas JMulTi seems to be using a VAR(k_ar+1) model.
References