statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults.get_prediction¶

DynamicFactorMQResults.get_prediction(start=None, end=None, dynamic=False, information_set='predicted', signal_only=False, original_scale=True, index=None, exog=None, extend_model=None, extend_kwargs=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 : bool, 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.

information_set : str, optional¶

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, …).

signal_only : bool, optional¶

Whether to compute forecasts 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 forecasts of the “signal” \(Z \alpha_t\) will be returned. Otherwise, the default is for forecasts of \(y_t\) to be returned.

original_scale : bool, optional¶

If the model specification standardized the data, whether or not to return predictions in the original scale of the data (i.e. before it was standardized by the model). Default is True.

**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:¶

ndarray