statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults.apply¶

DynamicFactorMQResults.apply(endog, k_endog_monthly=None, endog_quarterly=None, refit=False, fit_kwargs=None, copy_initialization=False, retain_standardization=True, **kwargs)[source]¶

Apply the fitted parameters to new data unrelated to the original data.

Creates a new result object using the current fitted parameters, applied to a completely new dataset that is assumed to be unrelated to 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.
k_endog_monthlyint, optional: If specifying a monthly/quarterly mixed frequency model in which the provided endog dataset contains both the monthly and quarterly data, this variable should be used to indicate how many of the variables are monthly.
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.
refitbool, optional: Whether to re-fit the parameters, using the new dataset. Default is False (so parameters from the current results object are used to create the new results object).
fit_kwargsdict, optional: Keyword arguments to pass to fit (if refit=True) or filter / smooth.
copy_initializationbool, optional: Whether or not to copy the initialization from the current results set to the new model. Default is False.
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.