class statsmodels.regression.recursive_ls.RecursiveLS(endog, exog, **kwargs)
[source]
Recursive least squares
Parameters: |
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Recursive least squares (RLS) corresponds to expanding window ordinary least squares (OLS).
This model applies the Kalman filter to compute recursive estimates of the coefficients and recursive residuals.
[*] | Durbin, James, and Siem Jan Koopman. 2012. Time Series Analysis by State Space Methods: Second Edition. Oxford University Press. |
filter ([return_ssm]) | Kalman filtering |
fit () | Fits the model by application of the 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 ([return_ssm]) | 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, **kwargs) | 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.regression.recursive_ls.RecursiveLS.html