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)

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.

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
diff_endog(new_endog[, tolerance])	Compute indices of revised and newly added observations
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_components([a, Pstar, Pinf, A, ...])	Initialize the statespace model with component matrices
initialize_diffuse()	Initialize the statespace model as diffuse
initialize_known(constant, stationary_cov)	Initialize the statespace model with a known distribution for the 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.
simulator(nsimulations[, rng])	Retrieve a simulator for the statespace model
smooth([smoother_output, smooth_method, ...])	Apply the Kalman smoother to the statespace model.

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])	Compute indices of revised and newly added observations
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_components([a, Pstar, Pinf, A, ...])	Initialize the statespace model with component matrices
initialize_diffuse()	Initialize the statespace model as diffuse
initialize_known(constant, stationary_cov)	Initialize the statespace model with a known distribution for the 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.
simulator(nsimulations[, rng])	Retrieve a simulator for the statespace model
smooth([smoother_output, smooth_method, ...])	Apply the Kalman smoother to the statespace model.

Properties

conserve_memory	(int) Memory conservation bitmask
design	(array) Design matrix: \(Z~(k\_endog \times k\_states \times nobs)\)
dtype	(dtype) Datatype of currently active representation matrices
endog	(array) The observation vector, alias for obs
filter_augmented	(bool) Flag for augmented Kalman filtering.
filter_chandrasekhar	(bool) Flag for filtering with Chandrasekhar recursions
filter_collapsed	(bool) Flag for Kalman filtering with collapsed observation vector
filter_concentrated	(bool) Flag for Kalman filtering with concentrated log-likelihood
filter_conventional	(bool) Flag for conventional Kalman filtering
filter_exact_initial	(bool) Flag for exact initial Kalman filtering.
filter_extended	(bool) Flag for extended Kalman filtering.
filter_method	(int) Filtering method bitmask
filter_methods
filter_square_root	(bool) Flag for square-root Kalman filtering.
filter_timing	(int) Filter timing
filter_univariate	(bool) Flag for univariate filtering of multivariate observation vector
filter_unscented	(bool) Flag for unscented Kalman filtering.
inversion_method	(int) Inversion method bitmask
inversion_methods
invert_cholesky	(bool) Flag for Cholesky inversion method
invert_lu	(bool) Flag for LU inversion method
invert_univariate	(bool) Flag for univariate inversion method (recommended)
memory_conserve	(bool) Flag to conserve the maximum amount of memory
memory_no_filtered	(bool) Flag to prevent storing filtered state and covariance matrices
memory_no_filtered_cov	(bool) Flag to prevent storing filtered state covariance matrices
memory_no_filtered_mean	(bool) Flag to prevent storing filtered states
memory_no_forecast	(bool) Flag to prevent storing all forecast-related output
memory_no_forecast_cov	(bool) Flag to prevent storing forecast error covariance matrices
memory_no_forecast_mean	(bool) Flag to prevent storing forecasts and forecast errors
memory_no_gain	(bool) Flag to prevent storing the Kalman gain matrices
memory_no_likelihood	(bool) Flag to prevent storing likelihood values for each observation
memory_no_predicted	(bool) Flag to prevent storing predicted state and covariance matrices
memory_no_predicted_cov	(bool) Flag to prevent storing predicted state covariance matrices
memory_no_predicted_mean	(bool) Flag to prevent storing predicted states
memory_no_smoothing	(bool) Flag to prevent storing values required for smoothing
memory_no_std_forecast	(bool) Flag to prevent storing standardized forecast errors
memory_options
memory_store_all	(bool) Flag for storing all intermediate results in memory (default)
obs	(array) Observation vector: \(y~(k\_endog \times nobs)\)
obs_cov	(array) Observation covariance matrix: \(H~(k\_endog \times k\_endog \times nobs)\)
obs_intercept	(array) Observation intercept: \(d~(k\_endog \times nobs)\)
prefix	(str) BLAS prefix of currently active representation matrices
selection	(array) Selection matrix: \(R~(k\_states \times k\_posdef \times nobs)\)
simulation_outputs
smooth_alternative	(bool) Flag for alternative (modified Bryson-Frazier) smoothing
smooth_classical	(bool) Flag for classical (see e.g. Anderson and Moore, 1979) smoothing.
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	(bool) Flag for Cholesky and linear solver inversion method (recommended)
solve_lu	(bool) Flag for LU and linear solver inversion method
stability_force_symmetry	(bool) Flag for enforcing covariance matrix symmetry
stability_method	(int) Stability method bitmask
stability_methods
state_cov	(array) State covariance matrix: \(Q~(k\_posdef \times k\_posdef \times nobs)\)
state_intercept	(array) State intercept: \(c~(k\_states \times nobs)\)
time_invariant	(bool) Whether or not currently active representation matrices are time-invariant
timing_init_filtered	(bool) Flag for the alternate timing convention (Kim and Nelson, 2012)
timing_init_predicted	(bool) Flag for the default timing convention (Durbin and Koopman, 2012)
timing_options
transition	(array) Transition matrix: \(T~(k\_states \times k\_states \times nobs)\)