statistics - Vector Autoregression with Python Statsmodels -

I am trying to implement the cause of multi-dimensional guneras in Python. For that matter, I am using Vector Authorship from Stats Mödel, but when I try to collaborate outside it gives me an empty matrix. Does anyone tell me what's really wrong?

NMP import as statistics stats_odels.tsa.vector_ar import var_model def multi_dim_granger (X_ts, Y_ts, order = 5, test = 'F-test'): "" "Multivirate granger criterion Input: X_ts: The first vector time series is TxK matrix time examples with T and K is the dimension Y_ts: TxAd matrix time with the T, the second vector time series and the K dimension command: VAr Process Test Fitting Number of Maximum Number for: Residual Print-test ts.shape VAR_model = var_model.VAR (Ts) ts = VAR_model.fit (ic = 'AIC') to test for matrix "" "ts = np.hstack ((x, y)) MaxLog = order) Returns T. Six X = N. P. Raymond Sundance (1000,2) Y = (N.P. Ranges (4000) * N.P. Randam Randen (4000)). Reshape ((1000,4)) multi_dim_granger (x, y)

you Test_causality can be used to know the cause of Gregor VARResults example method. See documentation and examples.