class statsmodels.tsa.statespace.mlemodel.MLEModel(endog, k_states, exog=None, dates=None, freq=None, **kwargs)
[source]
State space model for maximum likelihood estimation
Parameters: |
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ssm
KalmanFilter – Underlying state space representation.
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).
See also
MLEResults
, statsmodels.tsa.statespace.kalman_filter.KalmanFilter
, statsmodels.tsa.statespace.representation.Representation
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 parameters |
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 |
simulation_smoother ([simulation_output]) | Retrieve a simulation smoother for 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 parameters used in likelihood evaluation |
untransform_params (constrained) | Transform constrained parameters used in likelihood evaluation to unconstrained parameters used by the optimizer |
update (params[, transformed, complex_step]) | Update the parameters of the model |
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 actually included in the model). |
start_params | (array) Starting parameters for maximum likelihood estimation. |
tolerance |
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.mlemodel.MLEModel.html