statsmodels.tsa.statespace.kalman_filter.FilterResults¶

class
statsmodels.tsa.statespace.kalman_filter.
FilterResults
(model)[source]¶ Results from applying the Kalman 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: int

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: array

design
¶ The design matrix, \(Z\).
Type: array

obs_intercept
¶ The intercept for the observation equation, \(d\).
Type: array

obs_cov
¶ The covariance matrix for the observation equation \(H\).
Type: array

transition
¶ The transition matrix, \(T\).
Type: array

state_intercept
¶ The intercept for the transition equation, \(c\).
Type: array

selection
¶ The selection matrix, \(R\).
Type: array

state_cov
¶ The covariance matrix for the state equation \(Q\).
Type: array

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

initial_diffuse_state_cov
¶ Diffuse 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: int

stability_method
¶ Bitmask representing the methods used to promote numerical stability in the Kalman filter recursions.
Type: int

tolerance
¶ The tolerance at which the Kalman filter determines convergence to steadystate.
Type: float

loglikelihood_burn
¶ The number of initial periods during which the loglikelihood is not recorded.
Type: int

filtered_state
¶ The filtered state vector at each time period.
Type: array

filtered_state_cov
¶ The filtered state covariance matrix at each time period.
Type: array

predicted_state
¶ The predicted state vector at each time period.
Type: array

predicted_state_cov
¶ The predicted state covariance matrix at each time period.
Type: array

forecast_error_diffuse_cov
¶ Diffuse forecast error covariance matrix at each time period.
Type: array

predicted_diffuse_state_cov
¶ The predicted diffuse state covariance matrix at each time period.
Type: array

kalman_gain
¶ The Kalman gain at each time period.
Type: array

forecasts
¶ The onestepahead forecasts of observations at each time period.
Type: array

forecasts_error
¶ The forecast errors at each time period.
Type: array

forecasts_error_cov
¶ The forecast error covariance matrices at each time period.
Type: array

llf_obs
¶ The loglikelihood values at each time period.
Type: array
Methods
predict
([start, end, dynamic])Insample and outofsample 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 Attributes
kalman_gain
Kalman gain matrices standardized_forecasts_error
Standardized forecast errors 