# statsmodels.regression.recursive_ls.RecursiveLSResults¶

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
modelRecursiveLS instance

The fitted model instance

Attributes
specificationdictionary

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

Methods

 append(endog[, exog, refit, fit_kwargs]) Recreate the results object with new data appended to the original data apply(endog[, exog, refit, fit_kwargs]) Apply the fitted parameters to new data unrelated to the original data conf_int([alpha, cols]) Construct confidence interval for the fitted parameters. cov_params([r_matrix, column, scale, cov_p, …]) Compute the variance/covariance matrix. extend(endog[, exog, fit_kwargs]) Recreate the results object for new data that extends the original data f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis. 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 impulse_responses([steps, impulse, …]) Impulse response function info_criteria(criteria[, method]) Information criteria initialize(model, params, **kwargs) Initialize (possibly re-initialize) a Results instance. load(fname) Load a pickled results instance 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 Remove data arrays, all nobs arrays from result and model. 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 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.

Properties

 aic (float) Akaike Information Criterion aicc (float) Akaike Information Criterion with small sample correction bic (float) Bayes Information Criterion bse The standard errors of the parameter estimates. centered_tss Centered tss 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 fittedvalues (array) The predicted values of the model. hqic (float) Hannan-Quinn Information Criterion 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 loglikelihood_burn (float) The number of observations during which the likelihood is not evaluated. mae (float) Mean absolute error mse (float) Mean squared error mse_model mse_resid mse_total pvalues (array) The p-values associated with the z-statistics of the coefficients. recursive_coefficients Estimates of regression coefficients, recursively estimated resid (array) The model residuals. resid_recursive Recursive residuals rsquared sse (float) Sum of squared errors ssr states tvalues Return the t-statistic for a given parameter estimate. uncentered_tss uncentered tss use_t Flag indicating to use the Student’s distribution in inference. zvalues (array) The z-statistics for the coefficients.