Recursive least squares
Parameters: | endog : array_like
exog : array_like
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Notes
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.
References
[R24] | Durbin, James, and Siem Jan Koopman. 2012. Time Series Analysis by State Space Methods: Second Edition. Oxford University Press. |
Methods
filter([return_ssm]) | |
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 |
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([return_ssm]) | |
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, **kwargs) | Update the parameters of the model |
Methods
filter([return_ssm]) | |
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 |
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([return_ssm]) | |
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, **kwargs) | Update the parameters of the model |
Attributes
endog_names | Names of endogenous variables |
exog_names | |
initial_variance | |
initialization | |
loglikelihood_burn | |
param_names | |
start_params | |
tolerance |