arch.unitroot.cointegration.DynamicOLSResults¶

class arch.unitroot.cointegration.DynamicOLSResults(params: Series, cov: DataFrame, resid: Series, lags: int, leads: int, cov_type: str, kernel_est: CovarianceEstimator, num_x: int, trend: 'n' | 'c' | 'ct' | 'ctt', reg_results: RegressionResults, df_adjust: bool)[source]¶

Estimation results for Dynamic OLS models

Parameters:¶

params: Series¶

The estimated model parameters.

cov: DataFrame¶

The estimated parameter covariance.

resid: Series¶

The model residuals.

lags: int¶

The number of lags included in the model.

leads: int¶

The number of leads included in the model.

cov_type: str¶

The type of the parameter covariance estimator used.

kernel_est: CovarianceEstimator¶

The covariance estimator instance used to estimate the parameter covariance.

reg_results: RegressionResults¶

Regression results from fitting statsmodels OLS.

df_adjust: bool¶

Whether to degree of freedom adjust the estimator.

Methods

summary([full])

Summary of the model, containing estimated parameters and std.

Properties

bandwidth	The bandwidth used in the parameter covariance estimation
cov	The estimated parameter covariance of the cointegrating vector
cov_type	The type of parameter covariance estimator used
full_cov	Parameter covariance of the all model parameters, incl.
full_params	The complete set of parameters, including leads and lags
kernel	The kernel used to estimate the covariance
lags	The number of lags included in the model
leads	The number of leads included in the model
long_run_variance	The long-run variance of the regression residual.
params	The estimated parameters of the cointegrating vector
pvalues	P-value of the parameters in the cointegrating vector
resid	The model residuals
residual_variance	The variance of the regression residual.
rsquared	The model R²
rsquared_adj	The degree-of-freedom adjusted R²
std_errors	Standard errors of the parameters in the cointegrating vector
tvalues	T-statistics of the parameters in the cointegrating vector