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)

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

Parameters

k_endogarray_like or integer

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

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
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.