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statsmodels.tsa.statespace.kalman_smoother.KalmanSmoother

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

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

Parameters:

k_endog : array_like or integer

The observed time-series process y if array like or the number of variables in the process if an integer.

k_states : int

The dimension of the unobserved state process.

k_posdef : int, 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_class : class, 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
endog
obs_cov
obs_intercept
selection
state_cov
state_intercept
transition

Methods

bind(endog) Bind data to the statespace representation
filter([filter_method, inversion_method, ...]) Apply the Kalman filter to the statespace model.
impulse_responses([steps, impulse, ...]) Impulse response function
initialize_approximate_diffuse([variance]) Initialize the statespace model with approximate diffuse values.
initialize_known(initial_state, ...) Initialize the statespace model with known distribution for initial state.
initialize_stationary() Initialize the statespace model as stationary.
loglike([loglikelihood_burn]) Calculate the loglikelihood associated with the statespace model.
loglikeobs([loglikelihood_burn]) 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_inversion_method([inversion_method]) Set the inversion 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, results, ...]) Apply the Kalman smoother to the statespace model.

Methods

bind(endog) Bind data to the statespace representation
filter([filter_method, inversion_method, ...]) Apply the Kalman filter to the statespace model.
impulse_responses([steps, impulse, ...]) Impulse response function
initialize_approximate_diffuse([variance]) Initialize the statespace model with approximate diffuse values.
initialize_known(initial_state, ...) Initialize the statespace model with known distribution for initial state.
initialize_stationary() Initialize the statespace model as stationary.
loglike([loglikelihood_burn]) Calculate the loglikelihood associated with the statespace model.
loglikeobs([loglikelihood_burn]) 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_inversion_method([inversion_method]) Set the inversion 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, results, ...]) Apply the Kalman smoother to the statespace model.

Attributes

conserve_memory int(x=0) -> int or long
design
dtype (dtype) Datatype of currently active representation matrices
endog
filter_conventional bool(x) -> bool
filter_method int(x=0) -> int or long
filter_methods list() -> new empty list
inversion_method int(x=0) -> int or long
inversion_methods list() -> new empty list
invert_cholesky bool(x) -> bool
invert_lu bool(x) -> bool
invert_univariate bool(x) -> bool
memory_conserve bool(x) -> bool
memory_no_filtered bool(x) -> bool
memory_no_forecast bool(x) -> bool
memory_no_likelihood bool(x) -> bool
memory_no_predicted bool(x) -> bool
memory_options list() -> new empty list
memory_store_all bool(x) -> bool
obs (array) Observation vector: y~(k\_endog \times nobs)
obs_cov
obs_intercept
prefix (str) BLAS prefix of currently active representation matrices
selection
smoother_all bool(x) -> bool
smoother_disturbance bool(x) -> bool
smoother_disturbance_cov bool(x) -> bool
smoother_output int(x=0) -> int or long
smoother_outputs list() -> new empty list
smoother_state bool(x) -> bool
smoother_state_cov bool(x) -> bool
solve_cholesky bool(x) -> bool
solve_lu bool(x) -> bool
stability_force_symmetry bool(x) -> bool
stability_method int(x=0) -> int or long
stability_methods list() -> new empty list
state_cov
state_intercept
time_invariant (bool) Whether or not currently active representation matrices are
transition

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