statsmodels.tsa.vector_ar.vecm.VECMResults.predict  
VECMResults.predict(steps=5, alpha=None, exog_fc=None, exog_coint_fc=None) [source]
 
Calculate future values of the time series.    
Parameters:

 
steps (int) – Prediction horizon. 
alpha (float, 0 < alpha < 1 or None) – If None, compute point forecast only. If float, compute confidence intervals too. In this case the argument stands for the confidence level. 
exog (ndarray (steps x self.exog.shape[1])) – If self.exog is not None, then information about the future values of exog have to be passed via this parameter. The ndarray may be larger in it’s first dimension. In this case only the first steps rows will be considered.    
Returns:

In case of a point forecast: each row of the returned ndarray represents the forecast of the neqs variables for a specific period. The first row (index [0]) is the forecast for the next period, the last row (index [steps-1]) is the steps-periods-ahead- forecast.   
Return type:

forecast - ndarray (steps x neqs) or three ndarrays    


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