statsmodels.regression.rolling.RollingRegressionResults¶
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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.