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

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_invariant bool Whether or not the representation matrices are time-invariant
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.
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.
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.
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.
llf_obs array The loglikelihood values at each time period.

Methods

predict([start, end, dynamic, full_results]) 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

Attributes

kalman_gain Kalman gain matrices
standardized_forecasts_error Standardized forecast errors