statsmodels.regression.rolling.RollingRegressionResults

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

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.
cov_params()	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()	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.