# 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

Number of observations.

Type

int

nobs_diffuse

Number of observations under the diffuse Kalman filter.

Type

int

k_endog

The dimension of the observation series.

Type

int

k_states

The dimension of the unobserved state process.

Type

int

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

prefix

BLAS prefix of representation matrices

Type

str

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

time_invariant

Whether or not the representation matrices are time-invariant

Type

bool

initialization

Kalman filter initialization method.

Type

str

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

filter_method

Bitmask representing the Kalman filtering method

Type

int

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

conserve_memory

Bitmask representing the selected memory conservation method.

Type

int

filter_timing

Whether or not to use the alternate timing convention.

Type

int

tolerance

The tolerance at which the Kalman filter determines convergence to steady-state.

Type

float

loglikelihood_burn

The number of initial periods during which the loglikelihood is not recorded.

Type

int

converged

Whether or not the Kalman filter converged.

Type

bool

period_converged

The time period in which the Kalman filter converged.

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 one-step-ahead 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]) 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