statsmodels.regression.recursive_ls.RecursiveLS¶
-
class
statsmodels.regression.recursive_ls.
RecursiveLS
(endog, exog, constraints=None, **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.
constraints : array-like, str, or tuple
- array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero.
- str : The full hypotheses to test can be given as a string. See the examples.
- tuple : A tuple of arrays in the form (R, q),
q
can be either a scalar or a length p row vector.
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. 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
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 approximate diffuse initialize_known
(initial_state, …)Initialize known initialize_statespace
(**kwargs)Initialize the state space representation initialize_stationary
()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