State space model for maximum likelihood estimation
Parameters: | endog : array_like
k_states : int
exog : array_like, optional
dates : array-like of datetime, optional
freq : str, optional
**kwargs :
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See also
MLEResults, statsmodels.tsa.statespace.kalman_filter.KalmanFilter, statsmodels.tsa.statespace.representation.Representation
Notes
This class wraps the state space model with Kalman filtering to add in functionality for maximum likelihood estimation. In particular, it adds the concept of updating the state space representation based on a defined set of parameters, through the update method or updater attribute (see below for more details on which to use when), and it adds a fit method which uses a numerical optimizer to select the parameters that maximize the likelihood of the model.
The start_params update method must be overridden in the child class (and the transform and untransform methods, if needed).
Attributes
ssm | KalmanFilter | Underlying state space representation. |
Methods
filter(params[, transformed, complex_step, ...]) | Kalman filtering |
fit([start_params, transformed, cov_type, ...]) | Fits the model by maximum likelihood via Kalman filter. |
from_formula(formula, data[, subset]) | Not implemented for state space models |
hessian(params, *args, **kwargs) | Hessian matrix of the likelihood function, evaluated at the given |
impulse_responses(params[, steps, impulse, ...]) | Impulse response function |
information(params) | Fisher information matrix of model |
initialize() | Initialize (possibly re-initialize) a Model instance. |
initialize_approximate_diffuse([variance]) | |
initialize_known(initial_state, ...) | |
initialize_statespace(**kwargs) | Initialize the state space representation |
initialize_stationary() | |
loglike(params, *args, **kwargs) | Loglikelihood evaluation |
loglikeobs(params[, transformed, complex_step]) | Loglikelihood evaluation |
observed_information_matrix(params[, ...]) | Observed information matrix |
opg_information_matrix(params[, ...]) | Outer product of gradients information matrix |
predict(params[, exog]) | After a model has been fit predict returns the fitted values. |
prepare_data() | Prepare data for use in the state space representation |
score(params, *args, **kwargs) | Compute the score function at params. |
score_obs(params[, method, transformed, ...]) | Compute the score per observation, evaluated at params |
set_conserve_memory([conserve_memory]) | Set the memory conservation method |
set_filter_method([filter_method]) | Set the filtering method |
set_inversion_method([inversion_method]) | Set the inversion method |
set_smoother_output([smoother_output]) | Set the smoother output |
set_stability_method([stability_method]) | Set the numerical stability method |
simulate(params, nsimulations[, ...]) | Simulate a new time series following the state space model |
smooth(params[, transformed, complex_step, ...]) | Kalman smoothing |
transform_jacobian(unconstrained[, ...]) | Jacobian matrix for the parameter transformation function |
transform_params(unconstrained) | Transform unconstrained parameters used by the optimizer to constrained |
untransform_params(constrained) | Transform constrained parameters used in likelihood evaluation |
update(params[, transformed, complex_step]) | Update the parameters of the model |
Methods
filter(params[, transformed, complex_step, ...]) | Kalman filtering |
fit([start_params, transformed, cov_type, ...]) | Fits the model by maximum likelihood via Kalman filter. |
from_formula(formula, data[, subset]) | Not implemented for state space models |
hessian(params, *args, **kwargs) | Hessian matrix of the likelihood function, evaluated at the given |
impulse_responses(params[, steps, impulse, ...]) | Impulse response function |
information(params) | Fisher information matrix of model |
initialize() | Initialize (possibly re-initialize) a Model instance. |
initialize_approximate_diffuse([variance]) | |
initialize_known(initial_state, ...) | |
initialize_statespace(**kwargs) | Initialize the state space representation |
initialize_stationary() | |
loglike(params, *args, **kwargs) | Loglikelihood evaluation |
loglikeobs(params[, transformed, complex_step]) | Loglikelihood evaluation |
observed_information_matrix(params[, ...]) | Observed information matrix |
opg_information_matrix(params[, ...]) | Outer product of gradients information matrix |
predict(params[, exog]) | After a model has been fit predict returns the fitted values. |
prepare_data() | Prepare data for use in the state space representation |
score(params, *args, **kwargs) | Compute the score function at params. |
score_obs(params[, method, transformed, ...]) | Compute the score per observation, evaluated at params |
set_conserve_memory([conserve_memory]) | Set the memory conservation method |
set_filter_method([filter_method]) | Set the filtering method |
set_inversion_method([inversion_method]) | Set the inversion method |
set_smoother_output([smoother_output]) | Set the smoother output |
set_stability_method([stability_method]) | Set the numerical stability method |
simulate(params, nsimulations[, ...]) | Simulate a new time series following the state space model |
smooth(params[, transformed, complex_step, ...]) | Kalman smoothing |
transform_jacobian(unconstrained[, ...]) | Jacobian matrix for the parameter transformation function |
transform_params(unconstrained) | Transform unconstrained parameters used by the optimizer to constrained |
untransform_params(constrained) | Transform constrained parameters used in likelihood evaluation |
update(params[, transformed, complex_step]) | Update the parameters of the model |
Attributes
endog_names | Names of endogenous variables |
exog_names | |
initial_variance | |
initialization | |
loglikelihood_burn | |
param_names | (list of str) List of human readable parameter names (for parameters |
start_params | (array) Starting parameters for maximum likelihood estimation. |
tolerance |