Results from applying the Kalman filter to a state space model.
Parameters: | model : Representation
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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 |