statsmodels.tsa.statespace.kalman_filter.FilterResults

class statsmodels.tsa.statespace.kalman_filter.FilterResults(model)

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])	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

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

Attributes

kalman_gain	Kalman gain matrices
standardized_forecasts_error	Standardized forecast errors