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_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.
- k_endog{array_like,
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 the statespace model as stationary.
initialize_known
(constant, stationary_cov)Initialize the statespace model with known distribution for initial state.
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
(dtype) Datatype of currently active representation matrices
(bool) Flag to prevent storing filtered state and covariance matrices.
(bool) Flag to prevent storing all forecast-related output.
(bool) Flag to prevent storing predicted state and covariance matrices.
(array) Observation vector: \(y~(k\_endog \times nobs)\)
(str) BLAS prefix of currently active representation matrices
(bool) Flag for alternative (modified Bryson-Frazier) smoothing.
(bool) Flag for classical (see e.g.
(bool) Flag for conventional (Durbin and Koopman, 2012) Kalman smoothing.
(bool) Flag for univariate smoothing (uses modified Bryson-Frazier timing).
(bool) Whether or not currently active representation matrices are time-invariant