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