statsmodels.regression.recursive_ls.RecursiveLSResults.forecast¶

RecursiveLSResults.forecast(steps=1, signal_only=False, **kwargs)¶

Out-of-sample forecasts

Parameters:¶

stepsint, str, or datetime, optional

If an integer, the number of steps to forecast from the end of the sample. Can also be a date string to parse or a datetime type. However, if the dates index does not have a fixed frequency, steps must be an integer. Default is 1.

signal_onlybool, 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.

**kwargs

Additional arguments may required for forecasting beyond the end of the sample. See FilterResults.predict for more details.

Returns:¶

forecastarray_like

Out-of-sample forecasts (Numpy array or Pandas Series or DataFrame, depending on input and dimensions). Dimensions are (steps x k_endog).

See also

predict

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

get_forecast

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

get_prediction

In-sample predictions / out-of-sample forecasts and results including confidence intervals.