statsmodels.regression.recursive_ls.RecursiveLS

class statsmodels.regression.recursive_ls.RecursiveLS(endog, exog, **kwargs)

Recursive least squares

Parameters:

endog : array_like

The observed time-series process

exog : array_like

Array of exogenous regressors, shaped nobs x k.

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.

Attributes

endog_names	Names of endogenous variables
exog_names
initial_variance
initialization
loglikelihood_burn
param_names
start_params
tolerance

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