statsmodels.regression.recursive_ls.RecursiveLSResults.get_prediction

RecursiveLSResults.get_prediction(start=None, end=None, dynamic=False, index=None, **kwargs)[source]

In-sample prediction and out-of-sample forecasting

Parameters:
  • start (int, str, or datetime, optional) – Zero-indexed observation number at which to start forecasting, i.e., the first forecast is start. Can also be a date string to parse or a datetime type. Default is the the zeroth observation.
  • end (int, str, or datetime, optional) – Zero-indexed observation number at which to end forecasting, i.e., the last forecast is end. Can also be a date string to parse or a datetime type. However, if the dates index does not have a fixed frequency, end must be an integer index if you want out of sample prediction. Default is the last observation in the sample.
  • dynamic (boolean, int, str, or datetime, optional) – Integer offset relative to start at which to begin dynamic prediction. Can also be an absolute date string to parse or a datetime type (these are not interpreted as offsets). Prior to this observation, true endogenous values will be used for prediction; starting with this observation and continuing through the end of prediction, forecasted endogenous values will be used instead.
  • **kwargs – Additional arguments may required for forecasting beyond the end of the sample. See FilterResults.predict for more details.
Returns:

forecast – Array of out of in-sample predictions and / or out-of-sample forecasts. An (npredict x k_endog) array.

Return type:

array