statsmodels.regression.recursive_ls.RecursiveLSResults.predict¶

RecursiveLSResults.predict(start=None, end=None, dynamic=False, **kwargs)¶

In-sample prediction and out-of-sample forecasting

Parameters:

start{int, str,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 zeroth observation.
end{int, str,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{bool, int, str,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 be required for forecasting beyond the end of the sample. See FilterResults.predict for more details.

Returns:

predictionsarray_like: In-sample predictions / Out-of-sample forecasts. (Numpy array or Pandas Series or DataFrame, depending on input and dimensions). Dimensions are (npredict x k_endog).