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 
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_invariant
¶ bool – Whether or not the representation matrices are timeinvariant

initialization
¶ str – Kalman filter initialization method.

initial_state
¶ array_like – The state vector used to initialize the Kalamn filter.

initial_state_cov
¶ array_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.

filter_timing
¶ int – Whether or not to use the alternate timing convention.

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

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

converged
¶ bool – 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 – The Kalman gain at each time period.

forecasts
¶ array – The onestepahead 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.

llf_obs
¶ array – The loglikelihood values at each time period.
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 