Recursive least squares
| Parameters: | endog : array_like
exog : array_like
|
|---|
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 |