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:

stepsint, optional: Prediction horizon.
alphafloat or None, optional: If None, compute point forecast only. If float, compute confidence intervals too. In this case the argument (which must satisfy 0 < alpha < 1) stands for the confidence level.
exog_fcndarray (steps x self.exog.shape[1]), optional: 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.
exog_coint_fcndarray (steps x self.exog_coint.shape[1]), optional: If self.exog_coint is not None, then information about the future values of exog_coint 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:

forecastndarray (steps x neqs) or three ndarrays: 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.