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:

model : RollingWLS

Model instance

store : RollingStore

Container for raw moving window results

k_constant : bool

Flag indicating that the model contains a constant

use_t : bool

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

cov_type : str

Name of covariance estimator

Attributes

cov_type 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.

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.