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_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_type
str Name of covariance estimator
- model
- 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_typeName 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.
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.
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
Akaike’s information criteria.
Bayes’ information criteria.
The standard errors of the parameter estimates.
The total (weighted) sum of squares centered about the mean.
Name of covariance estimator
The model degree of freedom.
The residual degree of freedom.
The explained sum of squares.
The p-value of the F-statistic.
F-statistic of the fully specified model.
Flag indicating whether the model contains a constant
Log-likelihood of model
Mean squared error the model.
Mean squared error of the residuals.
Total mean squared error.
Number of observations n.
Estimated model parameters
The two-tailed p values for the t-stats of the params.
R-squared of the model.
Adjusted R-squared.
Sum of squared (whitened) residuals.
Return the t-statistic for a given parameter estimate.
Uncentered sum of squares.
Flag indicating to use the Student’s distribution in inference.