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

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

Parameters:model (RecursiveLS instance) – The fitted model instance

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



aic() (float) Akaike Information Criterion
bic() (float) Bayes Information Criterion
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
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)
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.
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
save(fname[, remove_data]) save a pickle of this instance
simulate(nsimulations[, measurement_shocks, …]) Simulate a new time series following the state space model
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.
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.


recursive_coefficients Estimates of regression coefficients, recursively estimated