statsmodels.tsa.statespace.kalman_smoother.KalmanSmoother¶

class statsmodels.tsa.statespace.kalman_smoother.KalmanSmoother(k_endog, k_states, k_posdef=None, results_class=None, kalman_smoother_classes=None, **kwargs)[source]¶

State space representation of a time series process, with Kalman filter and 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.
results_classclass, optional: Default results class to use to save filtering output. Default is SmootherResults. If specified, class must extend from SmootherResults.
**kwargs: Keyword arguments may be used to provide default values for state space matrices, for Kalman filtering options, or for Kalman smoothing options. See Representation for more details.

Attributes

design
dtype: (dtype) Datatype of currently active representation matrices
endog
memory_no_filtered: (bool) Flag to prevent storing filtered state and covariance matrices.
memory_no_forecast: (bool) Flag to prevent storing all forecast-related output.
memory_no_predicted: (bool) Flag to prevent storing predicted state and covariance matrices.
obs: (array) Observation vector: \(y~(k\_endog \times nobs)\)
obs_cov
obs_intercept
prefix: (str) BLAS prefix of currently active representation matrices
selection
state_cov
state_intercept
time_invariant: (bool) Whether or not currently active representation matrices are
transition

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
`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
`smooth`([smoother_output, smooth_method, …])	Apply the Kalman smoother to the statespace model.

diff_endog

Methods

`bind`(endog)	Bind data to the statespace representation
`clone`(endog, **kwargs)	Clone a state space representation while overriding some elements
`diff_endog`(new_endog[, tolerance])
`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
`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
`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_chandrasekhar`
`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`
`smooth_alternative`	(bool) Flag for alternative (modified Bryson-Frazier) smoothing.
`smooth_classical`	(bool) Flag for classical (see e.g.
`smooth_conventional`	(bool) Flag for conventional (Durbin and Koopman, 2012) Kalman smoothing.
`smooth_method`
`smooth_methods`
`smooth_univariate`	(bool) Flag for univariate smoothing (uses modified Bryson-Frazier timing).
`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`