statsmodels.regression.recursive_ls.RecursiveLS.filter#
- RecursiveLS.filter(return_ssm=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.
- includes_fixedbool,
optional If parameters were previously fixed with the fix_params method, this argument describes whether or not params also includes the fixed parameters, in addition to the free parameters. Default is False.
- complex_stepbool,
optional Whether or not to compute the filtered output using complex step differentiation. Default is False.
- 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.
- cov_kwds
dictorNone,optional See MLEResults.get_robustcov_results for a description required keywords for alternative covariance estimators
- results_class
type,optional A results class to use for results object. Default is MLEResults.
- results_wrapper_class
type,optional A results wrapper class to use for the results object. Default is MLEResultsWrapper.
- 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.