statsmodels.tsa.statespace.simulation_smoother.SimulationSmoother¶

class statsmodels.tsa.statespace.simulation_smoother.SimulationSmoother(k_endog, k_states, k_posdef=None, simulation_smooth_results_class=None, simulation_smoother_classes=None, **kwargs)[source]¶

State space representation of a time series process, with Kalman filter and smoother, and with simulation smoother.

Parameters

k_endog{array_like, int}: The observed time-series process \(y\) if array like or the number of variables in the process if an integer.
k_statesint: The dimension of the unobserved state process.
k_posdefint, optional: The dimension of a guaranteed positive definite covariance matrix describing the shocks in the measurement equation. Must be less than or equal to k_states. Default is k_states.
simulation_smooth_results_classclass, optional: Default results class to use to save output of simulation smoothing. Default is SimulationSmoothResults. If specified, class must extend from SimulationSmoothResults.
simulation_smoother_classesdict, optional: Dictionary with BLAS prefixes as keys and classes as values.
**kwargs: Keyword arguments may be used to provide default values for state space matrices, for Kalman filtering options, for Kalman smoothing options, or for Simulation smoothing options. See Representation, KalmanFilter, and KalmanSmoother for more details.

Methods

`bind`(endog)	Bind data to the statespace representation
`clone`(endog, **kwargs)	Clone a state space representation while overriding some elements
`extend`(endog[, start, end])	Extend the current state space model, or a specific (time) subset
`filter`([filter_method, inversion_method, …])	Apply the Kalman filter to the statespace model.
`fixed_scale`(scale)	Context manager for fixing the scale when FILTER_CONCENTRATED is set
`get_simulation_output`([simulation_output, …])	Get simulation output bitmask
`impulse_responses`([steps, impulse, …])	Impulse response function
`initialize`(initialization[, …])	Create an Initialization object if necessary
`initialize_approximate_diffuse`([variance])	Initialize the statespace model with approximate diffuse values.
`initialize_diffuse`()	Initialize the statespace model as stationary.
`initialize_known`(constant, stationary_cov)	Initialize the statespace model with known distribution for initial state.
`initialize_stationary`()	Initialize the statespace model as stationary.
`loglike`(**kwargs)	Calculate the loglikelihood associated with the statespace model.
`loglikeobs`(**kwargs)	Calculate the loglikelihood for each observation associated with the statespace model.
`set_conserve_memory`([conserve_memory])	Set the memory conservation method
`set_filter_method`([filter_method])	Set the filtering method
`set_filter_timing`([alternate_timing])	Set the filter timing convention
`set_inversion_method`([inversion_method])	Set the inversion method
`set_smooth_method`([smooth_method])	Set the smoothing method
`set_smoother_output`([smoother_output])	Set the smoother output
`set_stability_method`([stability_method])	Set the numerical stability method
`simulate`(nsimulations[, measurement_shocks, …])	Simulate a new time series following the state space model
`simulation_smoother`([simulation_output, …])	Retrieve a simulation smoother for the statespace model.
`smooth`([smoother_output, smooth_method, …])	Apply the Kalman smoother to the statespace model.

Properties

`conserve_memory`
`design`
`dtype`	(dtype) Datatype of currently active representation matrices
`endog`
`filter_augmented`
`filter_collapsed`
`filter_concentrated`
`filter_conventional`
`filter_exact_initial`
`filter_extended`
`filter_method`
`filter_methods`
`filter_square_root`
`filter_timing`
`filter_univariate`
`filter_unscented`
`inversion_method`
`inversion_methods`
`invert_cholesky`
`invert_lu`
`invert_univariate`
`memory_conserve`
`memory_no_filtered`	(bool) Flag to prevent storing filtered state and covariance matrices.
`memory_no_filtered_cov`
`memory_no_filtered_mean`
`memory_no_forecast`	(bool) Flag to prevent storing all forecast-related output.
`memory_no_forecast_cov`
`memory_no_forecast_mean`
`memory_no_gain`
`memory_no_likelihood`
`memory_no_predicted`	(bool) Flag to prevent storing predicted state and covariance matrices.
`memory_no_predicted_cov`
`memory_no_predicted_mean`
`memory_no_smoothing`
`memory_no_std_forecast`
`memory_options`
`memory_store_all`
`obs`	(array) Observation vector: \(y~(k\_endog \times nobs)\)
`obs_cov`
`obs_intercept`
`prefix`	(str) BLAS prefix of currently active representation matrices
`selection`
`simulation_outputs`
`smooth_alternative`
`smooth_classical`
`smooth_conventional`
`smooth_method`
`smooth_methods`
`smooth_univariate`
`smoother_all`
`smoother_disturbance`
`smoother_disturbance_cov`
`smoother_output`
`smoother_outputs`
`smoother_state`
`smoother_state_autocov`
`smoother_state_cov`
`solve_cholesky`
`solve_lu`
`stability_force_symmetry`
`stability_method`
`stability_methods`
`state_cov`
`state_intercept`
`time_invariant`	(bool) Whether or not currently active representation matrices are time-invariant
`timing_init_filtered`
`timing_init_predicted`
`timing_options`
`transition`