statsmodels.regression.rolling.RollingRegressionResults

class statsmodels.regression.rolling.RollingRegressionResults(model, store, 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

Attributes
aic

Akaike’s information criteria.

For a model with a constant \(-2llf + 2(df\_model + 1)\). For a model without a constant \(-2llf + 2(df\_model)\).

bic

Bayes’ information criteria.

For a model with a constant \(-2llf + \log(n)(df\_model+1)\). For a model without a constant \(-2llf + \log(n)(df\_model)\).

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.

The dof is defined as the rank of the regressor matrix minus 1 if a constant is included.

df_resid

The residual degree of freedom.

The dof is defined as the number of observations minus the rank of the regressor matrix.

ess

The explained sum of squares.

If a constant is present, the centered total sum of squares minus the sum of squared residuals. If there is no constant, the uncentered total sum of squares is used.

f_pvalue

The p-value of the F-statistic.

fvalue

F-statistic of the fully specified model.

Calculated as the mean squared error of the model divided by the mean squared error of the residuals if the nonrobust covariance is used. Otherwise computed using a Wald-like quadratic form that tests whether all coefficients (excluding the constant) are zero.

k_constant

Flag indicating whether the model contains a constant

llf

Log-likelihood of model

mse_model

Mean squared error the model.

The explained sum of squares divided by the model degrees of freedom.

mse_resid

Mean squared error of the residuals.

The sum of squared residuals divided by the residual degrees of freedom.

mse_total

Total mean squared error.

The uncentered total sum of squares divided by the number of observations.

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.

This is defined here as 1 - ssr/centered_tss if the constant is included in the model and 1 - ssr/uncentered_tss if the constant is omitted.

rsquared_adj

Adjusted R-squared.

This is defined here as 1 - (nobs-1)/df_resid * (1-rsquared) if a constant is included and 1 - nobs/df_resid * (1-rsquared) if no constant is included.

ssr

Sum of squared (whitened) residuals.

tvalues

Return the t-statistic for a given parameter estimate.

uncentered_tss

Uncentered sum of squares.

The sum of the squared values of the (whitened) endogenous response variable.

use_t

Flag indicating to use the Student’s distribution in inference.

Methods

conf_int([alpha, cols])

Construct confidence interval for the fitted parameters.

cov_params()

Estimated parameter covariance

info_criteria(crit[, dk_params])

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.