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)[source]¶ 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_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.
- simulation_smooth_results_class
class,optional Default results class to use to save output of simulation smoothing. Default is SimulationSmoothResults. If specified, class must extend from SimulationSmoothResults.
- simulation_smoother_classes
dict,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.
- k_endog{array_like,
- 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])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 the statespace model as diffuse.
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
simulation_smoother([simulation_output, ...])Retrieve a simulation smoother for the statespace model.
simulator(nsimulations[, random_state])smooth([smoother_output, smooth_method, ...])Apply the Kalman smoother to the statespace model.
Properties
(int) Memory conservation bitmask.
(array) Design matrix: \(Z~(k\_endog \times k\_states \times nobs)\)
(dtype) Datatype of currently active representation matrices
(array) The observation vector, alias for obs.
(bool) Flag for augmented Kalman filtering.
(bool) Flag for filtering with Chandrasekhar recursions.
(bool) Flag for Kalman filtering with collapsed observation vector.
(bool) Flag for Kalman filtering with concentrated log-likelihood.
(bool) Flag for conventional Kalman filtering.
(bool) Flag for exact initial Kalman filtering.
(bool) Flag for extended Kalman filtering.
(int) Filtering method bitmask.
(bool) Flag for square-root Kalman filtering.
(int) Filter timing.
(bool) Flag for univariate filtering of multivariate observation vector.
(bool) Flag for unscented Kalman filtering.
(int) Inversion method bitmask.
(bool) Flag for Cholesky inversion method.
(bool) Flag for LU inversion method.
(bool) Flag for univariate inversion method (recommended).
(bool) Flag to conserve the maximum amount of memory.
(bool) Flag to prevent storing filtered state and covariance matrices.
(bool) Flag to prevent storing filtered state covariance matrices.
(bool) Flag to prevent storing filtered states.
(bool) Flag to prevent storing all forecast-related output.
(bool) Flag to prevent storing forecast error covariance matrices.
(bool) Flag to prevent storing forecasts and forecast errors.
(bool) Flag to prevent storing the Kalman gain matrices.
(bool) Flag to prevent storing likelihood values for each observation.
(bool) Flag to prevent storing predicted state and covariance matrices.
(bool) Flag to prevent storing predicted state covariance matrices.
(bool) Flag to prevent storing predicted states.
(bool) Flag to prevent storing likelihood values for each observation.
(bool) Flag to prevent storing standardized forecast errors.
(bool) Flag for storing all intermediate results in memory (default).
(array) Observation vector: \(y~(k\_endog \times nobs)\)
(array) Observation covariance matrix: \(H~(k\_endog \times k\_endog \times nobs)\)
(array) Observation intercept: \(d~(k\_endog \times nobs)\)
(str) BLAS prefix of currently active representation matrices
(array) Selection matrix: \(R~(k\_states \times k\_posdef \times nobs)\)
(bool) Flag for alternative (modified Bryson-Frazier) smoothing.
(bool) Flag for classical (see e.g. Anderson and Moore, 1979) smoothing.
(bool) Flag for conventional (Durbin and Koopman, 2012) Kalman smoothing.
(bool) Flag for univariate smoothing (uses modified Bryson-Frazier timing).
(bool) Flag for Cholesky and linear solver inversion method (recommended).
(bool) Flag for LU and linear solver inversion method.
(bool) Flag for enforcing covariance matrix symmetry
(int) Stability method bitmask.
(array) State covariance matrix: \(Q~(k\_posdef \times k\_posdef \times nobs)\)
(array) State intercept: \(c~(k\_states \times nobs)\)
(bool) Whether or not currently active representation matrices are time-invariant
(bool) Flag for the alternate timing convention (Kim and Nelson, 2012).
(bool) Flag for the default timing convention (Durbin and Koopman, 2012).
(array) Transition matrix: \(T~(k\_states \times k\_states \times nobs)\)