statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQ.filter¶

DynamicFactorMQ.filter(params, transformed=True, includes_fixed=False, complex_step=False, cov_type='none', cov_kwds=None, return_ssm=False, results_class=None, results_wrapper_class=None, low_memory=False, **kwargs)[source]¶

Kalman filtering.

Parameters:¶

paramsarray_like

Array of parameters at which to evaluate the loglikelihood function.

transformedbool, optional

Whether or not params is already transformed. Default is True.

return_ssmbool,optional

Whether or not to return only the state space output or a full results object. Default is to return a full results object.

cov_typestr, optional

See MLEResults.fit for a description of covariance matrix types for results object. Default is ‘none’.

cov_kwdsdict or None, optional

See MLEResults.get_robustcov_results for a description required keywords for alternative covariance estimators

low_memorybool, optional

If set to True, techniques are applied to substantially reduce memory usage. If used, some features of the results object will not be available (including in-sample prediction), although out-of-sample forecasting is possible. Default is False.

**kwargs

Additional keyword arguments to pass to the Kalman filter. See KalmanFilter.filter for more details.