# statsmodels.regression.rolling.RollingRegressionResults¶

class statsmodels.regression.rolling.RollingRegressionResults(model, store: statsmodels.regression.rolling.RollingStore, k_constant, use_t, cov_type)[source]

Results from rolling regressions

Parameters
modelRollingWLS

Model instance

storeRollingStore

Container for raw moving window results

k_constantbool

Flag indicating that the model contains a constant

use_tbool

Flag indicating to use the Student’s t distribution when computing p-values.

cov_typestr

Name of covariance estimator

Methods

 conf_int([alpha, cols]) Construct confidence interval for the fitted parameters. Estimated parameter covariance load(fname) Load a pickled results instance plot_recursive_coefficient([variables, …]) Plot the recursively estimated coefficients on a given variable Remove data arrays, all nobs arrays from result and model. save(fname[, remove_data]) Save a pickle of this instance.

Properties

 aic Akaike’s information criteria. bic Bayes’ information criteria. bse The standard errors of the parameter estimates. centered_tss The total (weighted) sum of squares centered about the mean. cov_type Name of covariance estimator df_model The model degree of freedom. df_resid The residual degree of freedom. ess The explained sum of squares. f_pvalue The p-value of the F-statistic. fvalue F-statistic of the fully specified model. k_constant Flag indicating whether the model contains a constant llf Log-likelihood of model mse_model Mean squared error the model. mse_resid Mean squared error of the residuals. mse_total Total mean squared error. nobs Number of observations n. params Estimated model parameters pvalues The two-tailed p values for the t-stats of the params. rsquared R-squared of the model. rsquared_adj Adjusted R-squared. ssr Sum of squared (whitened) residuals. tvalues Return the t-statistic for a given parameter estimate. uncentered_tss Uncentered sum of squares. use_t Flag indicating to use the Student’s distribution in inference.