statsmodels.tsa.statespace.kalman_filter.FilterResults¶
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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 -
k_posdef
¶ The dimension of a guaranteed positive definite covariance matrix describing the shocks in the measurement equation.
Type: int
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dtype
¶ Datatype of representation matrices
Type: dtype
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shapes
¶ A dictionary recording the shapes of each of the representation matrices as tuples.
Type: dictionary of name,tuple
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endog
¶ The observation vector.
Type: array
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design
¶ The design matrix, \(Z\).
Type: array
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obs_intercept
¶ The intercept for the observation equation, \(d\).
Type: array
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obs_cov
¶ The covariance matrix for the observation equation \(H\).
Type: array
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transition
¶ The transition matrix, \(T\).
Type: array
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state_intercept
¶ The intercept for the transition equation, \(c\).
Type: array
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selection
¶ The selection matrix, \(R\).
Type: array
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state_cov
¶ The covariance matrix for the state equation \(Q\).
Type: array
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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
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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
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initial_state
¶ The state vector used to initialize the Kalamn filter.
Type: array_like
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initial_state_cov
¶ The state covariance matrix used to initialize the Kalamn filter.
Type: array_like
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inversion_method
¶ Bitmask representing the method used to invert the forecast error covariance matrix.
Type: int
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stability_method
¶ Bitmask representing the methods used to promote numerical stability in the Kalman filter recursions.
Type: int
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tolerance
¶ The tolerance at which the Kalman filter determines convergence to steady-state.
Type: float
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loglikelihood_burn
¶ The number of initial periods during which the loglikelihood is not recorded.
Type: int
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filtered_state
¶ The filtered state vector at each time period.
Type: array
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filtered_state_cov
¶ The filtered state covariance matrix at each time period.
Type: array
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predicted_state
¶ The predicted state vector at each time period.
Type: array
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predicted_state_cov
¶ The predicted state covariance matrix at each time period.
Type: array
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kalman_gain
¶ The Kalman gain at each time period.
Type: array
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forecasts
¶ The one-step-ahead forecasts of observations at each time period.
Type: array
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forecasts_error
¶ The forecast errors at each time period.
Type: array
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forecasts_error_cov
¶ The forecast error covariance matrices at each time period.
Type: array
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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 -