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
- k_posdef¶
The dimension of a guaranteed positive definite covariance matrix describing the shocks in the measurement equation.
- Type:
- dtype¶
Datatype of representation matrices
- Type:
dtype
- shapes¶
A dictionary recording the shapes of each of the representation matrices as tuples.
- Type:
dictionary of name:tuple
- endog¶
The observation vector.
- Type:
ndarray
- design¶
The design matrix, \(Z\).
- Type:
ndarray
- obs_intercept¶
The intercept for the observation equation, \(d\).
- Type:
ndarray
- obs_cov¶
The covariance matrix for the observation equation \(H\).
- Type:
ndarray
- transition¶
The transition matrix, \(T\).
- Type:
ndarray
- state_intercept¶
The intercept for the transition equation, \(c\).
- Type:
ndarray
- selection¶
The selection matrix, \(R\).
- Type:
ndarray
- state_cov¶
The covariance matrix for the state equation \(Q\).
- Type:
ndarray
- missing¶
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.
- Type:
array of bool
- nmissing¶
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.
- Type:
array of int
- initial_state¶
The state vector used to initialize the Kalamn filter.
- Type:
array_like
- initial_state_cov¶
The state covariance matrix used to initialize the Kalamn filter.
- Type:
array_like
- inversion_method¶
Bitmask representing the method used to invert the forecast error covariance matrix.
- Type:
- stability_method¶
Bitmask representing the methods used to promote numerical stability in the Kalman filter recursions.
- Type:
- tolerance¶
The tolerance at which the Kalman filter determines convergence to steady-state.
- Type:
- loglikelihood_burn¶
The number of initial periods during which the loglikelihood is not recorded.
- Type:
- filtered_state¶
The filtered state vector at each time period.
- Type:
ndarray
- filtered_state_cov¶
The filtered state covariance matrix at each time period.
- Type:
ndarray
- predicted_state¶
The predicted state vector at each time period.
- Type:
ndarray
- predicted_state_cov¶
The predicted state covariance matrix at each time period.
- Type:
ndarray
- kalman_gain¶
The Kalman gain at each time period.
- Type:
ndarray
- forecasts¶
The one-step-ahead forecasts of observations at each time period.
- Type:
ndarray
- forecasts_error¶
The forecast errors at each time period.
- Type:
ndarray
- forecasts_error_cov¶
The forecast error covariance matrices at each time period.
- Type:
ndarray
- loglikelihood¶
The loglikelihood values at each time period.
- Type:
ndarray
- collapsed_forecasts¶
If filtering using collapsed observations, stores the one-step-ahead forecasts of collapsed observations at each time period.
- Type:
ndarray
- collapsed_forecasts_error¶
If filtering using collapsed observations, stores the one-step-ahead forecast errors of collapsed observations at each time period.
- Type:
ndarray
- collapsed_forecasts_error_cov¶
If filtering using collapsed observations, stores the one-step-ahead forecast error covariance matrices of collapsed observations at each time period.
- Type:
ndarray
- standardized_forecast_error¶
The standardized forecast errors
- Type:
ndarray
- scaled_smoothed_estimator¶
The scaled smoothed estimator at each time period.
- Type:
ndarray
- scaled_smoothed_estimator_cov¶
The scaled smoothed estimator covariance matrices at each time period.
- Type:
ndarray
- smoothing_error¶
The smoothing error covariance matrices at each time period.
- Type:
ndarray
- smoothed_state¶
The smoothed state at each time period.
- Type:
ndarray
- smoothed_state_cov¶
The smoothed state covariance matrices at each time period.
- Type:
ndarray
- smoothed_state_autocov¶
The smoothed state lago-one autocovariance matrices at each time period: \(Cov(\alpha_{t+1}, \alpha_t)\).
- Type:
ndarray
- smoothed_measurement_disturbance¶
The smoothed measurement at each time period.
- Type:
ndarray
- smoothed_state_disturbance¶
The smoothed state at each time period.
- Type:
ndarray
- smoothed_measurement_disturbance_cov¶
The smoothed measurement disturbance covariance matrices at each time period.
- Type:
ndarray
- smoothed_state_disturbance_cov¶
The smoothed state disturbance covariance matrices at each time period.
- Type:
ndarray
Methods
get_smoothed_decomposition([...])Decompose smoothed output into contributions from observations
news(previous[, t, start, end, ...])Compute the news and impacts associated with a data release
predict([start, end, dynamic])In-sample and out-of-sample prediction for state space models generally
smoothed_state_autocovariance([lag, t, ...])Compute state vector autocovariances, conditional on the full dataset
smoothed_state_gain(updates_ix[, t, start, ...])Cov(tilde alpha_{t}, I) Var(I, I)^{-1}
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
