statsmodels.tsa.statespace.kalman_smoother.SmootherResults¶
-
class
statsmodels.tsa.statespace.kalman_smoother.
SmootherResults
(model)[source]¶ Results from applying the Kalman smoother and/or filter to a state space model.
- Parameters
- model
Representation
A Statespace representation
- model
- Attributes
- nobs
int
Number of observations.
- k_endog
int
The dimension of the observation series.
- k_states
int
The dimension of the unobserved state process.
- k_posdef
int
The dimension of a guaranteed positive definite covariance matrix describing the shocks in the measurement equation.
- dtype
dtype
Datatype of representation matrices
- prefix
str
BLAS prefix of representation matrices
- shapes
dictionary
of
name:tuple A dictionary recording the shapes of each of the representation matrices as tuples.
- endog
array
The observation vector.
- design
array
The design matrix, \(Z\).
- obs_intercept
array
The intercept for the observation equation, \(d\).
- obs_cov
array
The covariance matrix for the observation equation \(H\).
- transition
array
The transition matrix, \(T\).
- state_intercept
array
The intercept for the transition equation, \(c\).
- selection
array
The selection matrix, \(R\).
- state_cov
array
The covariance matrix for the state equation \(Q\).
- missing
array
of
bool An array of the same size as endog, filled with boolean values that are True if the corresponding entry in endog is NaN and False otherwise.
- nmissing
array
of
int
An array of size nobs, where the ith entry is the number (between 0 and k_endog) of NaNs in the ith row of the endog array.
- time_invariantbool
Whether or not the representation matrices are time-invariant
- initialization
str
Kalman filter initialization method.
- initial_statearray_like
The state vector used to initialize the Kalamn filter.
- initial_state_covarray_like
The state covariance matrix used to initialize the Kalamn filter.
- filter_method
int
Bitmask representing the Kalman filtering method
- inversion_method
int
Bitmask representing the method used to invert the forecast error covariance matrix.
- stability_method
int
Bitmask representing the methods used to promote numerical stability in the Kalman filter recursions.
- conserve_memory
int
Bitmask representing the selected memory conservation method.
- tolerance
float
The tolerance at which the Kalman filter determines convergence to steady-state.
- loglikelihood_burn
int
The number of initial periods during which the loglikelihood is not recorded.
- convergedbool
Whether or not the Kalman filter converged.
- period_converged
int
The time period in which the Kalman filter converged.
- filtered_state
array
The filtered state vector at each time period.
- filtered_state_cov
array
The filtered state covariance matrix at each time period.
- predicted_state
array
The predicted state vector at each time period.
- predicted_state_cov
array
The predicted state covariance matrix at each time period.
kalman_gain
array
Kalman gain matrices
- forecasts
array
The one-step-ahead forecasts of observations at each time period.
- forecasts_error
array
The forecast errors at each time period.
- forecasts_error_cov
array
The forecast error covariance matrices at each time period.
- loglikelihood
array
The loglikelihood values at each time period.
- collapsed_forecasts
array
If filtering using collapsed observations, stores the one-step-ahead forecasts of collapsed observations at each time period.
- collapsed_forecasts_error
array
If filtering using collapsed observations, stores the one-step-ahead forecast errors of collapsed observations at each time period.
- collapsed_forecasts_error_cov
array
If filtering using collapsed observations, stores the one-step-ahead forecast error covariance matrices of collapsed observations at each time period.
- standardized_forecast_error
array
The standardized forecast errors
- smoother_output
int
Bitmask representing the generated Kalman smoothing output
- scaled_smoothed_estimator
array
The scaled smoothed estimator at each time period.
- scaled_smoothed_estimator_cov
array
The scaled smoothed estimator covariance matrices at each time period.
- smoothing_error
array
The smoothing error covariance matrices at each time period.
- smoothed_state
array
The smoothed state at each time period.
- smoothed_state_cov
array
The smoothed state covariance matrices at each time period.
- smoothed_state_autocov
array
The smoothed state lago-one autocovariance matrices at each time period: \(Cov(\alpha_{t+1}, \alpha_t)\).
- smoothed_measurement_disturbance
array
The smoothed measurement at each time period.
- smoothed_state_disturbance
array
The smoothed state at each time period.
- smoothed_measurement_disturbance_cov
array
The smoothed measurement disturbance covariance matrices at each time period.
- smoothed_state_disturbance_cov
array
The smoothed state disturbance covariance matrices at each time period.
- nobs
Methods
predict
([start, end, dynamic])In-sample and out-of-sample prediction for state space models generally
update_filter
(kalman_filter)Update the filter results
update_representation
(model[, only_options])Update the results to match a given model
update_smoother
(smoother)Update the smoother results
Properties
Kalman gain matrices
Standardized forecast errors