statsmodels.regression.recursive_ls.RecursiveLS¶

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

Recursive least squares

Parameters: endog (array_like) – The observed time-series process $$y$$ 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

 [*] Durbin, James, and Siem Jan Koopman. 2012. Time Series Analysis by State Space Methods: Second Edition. Oxford University Press.

Methods

 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

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 actually included in the model). start_params (array) Starting parameters for maximum likelihood estimation. tolerance