statsmodels.regression.recursive_ls.RecursiveLSResults.get_prediction

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

In-sample prediction and out-of-sample forecasting

Parameters:

startint, 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.

endint, 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.

dynamicbool, 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:

predictionsPredictionResults

PredictionResults instance containing in-sample predictions / out-of-sample forecasts and results including confidence intervals.

See also

forecast

Out-of-sample forecasts.

predict

In-sample predictions and out-of-sample forecasts.

get_forecast

Out-of-sample forecasts and results including confidence intervals.