Python Statsmodels的向量自回归

我试图在python中实现多维Granger因果关系。就这点而言，我使用的是Statsmodels的向量自回归，但当我试图从中得到系数时，它会返回一个空矩阵。有人能告诉我到底出了什么问题吗

import numpy as np
from statsmodels.tsa.vector_ar import var_model
def multi_dim_granger(X_ts,Y_ts,order=5,test='F-test'):
    """Multivariate Granger cusality.
    input:
        X_ts: the first vector time series. 
              TxK matrix with T being the time instance and K is the dimension  
        Y_ts: the second vector time series. 
              TxK matrix with T being the time instance and K is the dimension  

        order: the maximum number of lags for fitting a VAR process
        test: the statistical test to check for the residual covariance matrix
    """
    ts=np.hstack((X,Y))
    print ts.shape
    VAR_model=var_model.VAR(ts)
    ts=VAR_model.fit(ic='aic',maxlags=order)
    return ts.coefs
X=np.random.randn(1000,2)
Y=(np.arange(4000)*np.random.randn(4000)).reshape((1000,4))
multi_dim_granger(X,Y)

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您可以使用VARResults实例的

test\u因果关系

方法来测试格兰杰因果关系。请参阅文档和示例