statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQ.filter¶
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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_type
str,optional See MLEResults.fit for a description of covariance matrix types for results object. Default is ‘none’.
- cov_kwds
dictorNone,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.