statsmodels.regression.recursive_ls.RecursiveLSResults¶
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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 -
specification¶ dictionary – Dictionary including all attributes from the recursive least squares model instance.
See also
statsmodels.tsa.statespace.kalman_filter.FilterResults,statsmodels.tsa.statespace.mlemodel.MLEResultsMethods
aic()(float) Akaike Information Criterion bic()(float) Bayes Information Criterion bse()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. load(fname)load a pickle, (class method) loglikelihood_burn()(float) The number of observations during which the likelihood is not evaluated. normalized_cov_params()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. Attributes
recursive_coefficientsEstimates of regression coefficients, recursively estimated use_t-