statsmodels.regression.recursive_ls.RecursiveLSResults

class statsmodels.regression.recursive_ls.RecursiveLSResults(model, params, filter_results, cov_type='opg', **kwargs)

Class to hold results from fitting a recursive least squares model.

Parameters

modelRecursiveLS instance

The fitted model instance

See also

statsmodels.tsa.statespace.kalman_filter.FilterResults, statsmodels.tsa.statespace.mlemodel.MLEResults

Attributes

specificationdictionary

Dictionary including all attributes from the recursive least squares model instance.

Methods

aic()	(float) Akaike Information Criterion
bic()	(float) Bayes Information Criterion
bse()	The standard errors of the parameter estimates.
centered_tss()	Centered tss
conf_int([alpha, cols, method])	Returns the confidence interval of the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, …])	Returns the variance/covariance matrix.
cov_params_approx()	(array) The variance / covariance matrix.
cov_params_oim()	(array) The variance / covariance matrix.
cov_params_opg()	(array) The variance / covariance matrix.
cov_params_robust()	(array) The QMLE variance / covariance matrix.
cov_params_robust_approx()	(array) The QMLE variance / covariance matrix.
cov_params_robust_oim()	(array) The QMLE variance / covariance matrix.
cusum()	Cumulative sum of standardized recursive residuals statistics
cusum_squares()	Cumulative sum of squares of standardized recursive residuals statistics
ess()	esss
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
fittedvalues()	(array) The predicted values of the model.
forecast([steps])	Out-of-sample forecasts
get_forecast([steps])	Out-of-sample forecasts
get_prediction([start, end, dynamic, index])	In-sample prediction and out-of-sample forecasting
hqic()	(float) Hannan-Quinn Information Criterion
impulse_responses([steps, impulse, …])	Impulse response function
info_criteria(criteria[, method])	Information criteria
initialize(model, params, **kwd)	Initialize (possibly re-initialize) a Results instance.
llf()	(float) The value of the log-likelihood function evaluated at params.
llf_obs()	(float) The value of the log-likelihood function evaluated at params.
llf_recursive()	(float) Loglikelihood defined by recursive residuals, equivalent to OLS
llf_recursive_obs()	(float) Loglikelihood at observation, computed from recursive residuals
load(fname)	load a pickle, (class method)
loglikelihood_burn()	(float) The number of observations during which the likelihood is not evaluated.
mse_model()
mse_resid()
mse_total()
normalized_cov_params()	See specific model class docstring
plot_cusum([alpha, legend_loc, fig, figsize])	Plot the CUSUM statistic and significance bounds.
plot_cusum_squares([alpha, legend_loc, fig, …])	Plot the CUSUM of squares statistic and significance bounds.
plot_diagnostics([variable, lags, fig, figsize])	Diagnostic plots for standardized residuals of one endogenous variable
plot_recursive_coefficient([variables, …])	Plot the recursively estimated coefficients on a given variable
predict([start, end, dynamic])	In-sample prediction and out-of-sample forecasting
pvalues()	(array) The p-values associated with the z-statistics of the coefficients.
remove_data()	remove data arrays, all nobs arrays from result and model
resid()	(array) The model residuals.
resid_recursive()	Recursive residuals
rsquared()
save(fname[, remove_data])	save a pickle of this instance
simulate(nsimulations[, measurement_shocks, …])	Simulate a new time series following the state space model
ssr()
summary([alpha, start, title, model_name, …])	Summarize the Model
t_test(r_matrix[, cov_p, scale, use_t])	Compute a t-test for a each linear hypothesis of the form Rb = q
t_test_pairwise(term_name[, method, alpha, …])	perform pairwise t_test with multiple testing corrected p-values
test_heteroskedasticity(method[, …])	Test for heteroskedasticity of standardized residuals
test_normality(method)	Test for normality of standardized residuals.
test_serial_correlation(method[, lags])	Ljung-box test for no serial correlation of standardized residuals
tvalues()	Return the t-statistic for a given parameter estimate.
uncentered_tss()	uncentered tss
wald_test(r_matrix[, cov_p, scale, invcov, …])	Compute a Wald-test for a joint linear hypothesis.
wald_test_terms([skip_single, …])	Compute a sequence of Wald tests for terms over multiple columns
zvalues()	(array) The z-statistics for the coefficients.