# 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

 (float) Akaike Information Criterion (float) Bayes Information Criterion The standard errors of the parameter estimates. 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. (array) The variance / covariance matrix. (array) The variance / covariance matrix. (array) The variance / covariance matrix. (array) The QMLE variance / covariance matrix. (array) The QMLE variance / covariance matrix. (array) The QMLE variance / covariance matrix. Cumulative sum of standardized recursive residuals statistics Cumulative sum of squares of standardized recursive residuals statistics esss f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis. (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 (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. (float) The value of the log-likelihood function evaluated at params. (float) The value of the log-likelihood function evaluated at params. (float) Loglikelihood defined by recursive residuals, equivalent to OLS (float) Loglikelihood at observation, computed from recursive residuals load(fname) load a pickle, (class method) (float) The number of observations during which the likelihood is not evaluated. 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 (array) The p-values associated with the z-statistics of the coefficients. remove data arrays, all nobs arrays from result and model (array) The model residuals. 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 Return the t-statistic for a given parameter estimate. 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 (array) The z-statistics for the coefficients.