statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults.extend¶

DynamicFactorMQResults.extend(endog, endog_quarterly=None, fit_kwargs=None, retain_standardization=True, **kwargs)[source]¶

Recreate the results object for new data that extends original data.

Creates a new result object applied to a new dataset that is assumed to follow directly from the end of the model’s original data. The new results can then be used for analysis or forecasting.

Parameters:¶

endogarray_like

New observations from the modeled time-series process.

endog_quarterlyarray_like, optional

New observations of quarterly variables. If provided, must be a Pandas Series or DataFrame with a DatetimeIndex or PeriodIndex at the quarterly frequency.

fit_kwargsdict, optional

Keyword arguments to pass to filter or smooth.

retain_standardizationbool, optional

Whether or not to use the mean and standard deviations that were used to standardize the data in the current model in the new model. Default is True.

**kwargs

Keyword arguments may be used to modify model specification arguments when created the new model object.

Returns:¶

results

Updated Results object, that includes results only for the new dataset.

See also

append

apply

Notes

The endog argument to this method should consist of new observations that occurred directly after the last element of the model’s original endog array. For any other kind of dataset, see the apply method.

This method will apply filtering only to the new data provided by the endog argument, which can be much faster than re-filtering the entire dataset. However, the returned results object will only have results for the new data. To retrieve results for both the new data and the original data, see the append method.