In-sample prediction and out-of-sample forecasting
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.
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.
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.
The information set to condition each prediction on. Default is “predicted”, which computes predictions of period t values conditional on observed data through period t-1; these are one-step-ahead predictions, and correspond with the typical fittedvalues results attribute. Alternatives are “filtered”, which computes predictions of period t values conditional on observed data through period t, and “smoothed”, which computes predictions of period t values conditional on the entire dataset (including also future observations t+1, t+2, …).
Whether to compute predictions of only the “signal” component of the observation equation. Default is False. For example, the observation equation of a time-invariant model is \(y_t = d + Z \alpha_t + \varepsilon_t\), and the “signal” component is then \(Z \alpha_t\). If this argument is set to True, then predictions of the “signal” \(Z \alpha_t\) will be returned. Otherwise, the default is for predictions of \(y_t\) to be returned.
Additional arguments may be required for forecasting beyond the end of the sample. See
FilterResults.predictfor more details.
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).
Out-of-sample forecasts and results including confidence intervals.
In-sample predictions / out-of-sample forecasts and results including confidence intervals.