arch.unitroot.cointegration.CanonicalCointegratingReg.fit¶
-
CanonicalCointegratingReg.fit(kernel: str =
'bartlett'
, bandwidth: float | None =None
, force_int: bool =True
, diff: bool =False
, df_adjust: bool =False
) CointegrationAnalysisResults [source]¶ Estimate the cointegrating vector.
- Parameters:¶
- diff: bool =
False
¶ Use differenced data to estimate the residuals.
- kernel: str =
'bartlett'
¶ The string name of any of any known kernel-based long-run covariance estimators. Common choices are “bartlett” for the Bartlett kernel (Newey-West), “parzen” for the Parzen kernel and “quadratic-spectral” for the Quadratic Spectral kernel.
- bandwidth: float | None =
None
¶ The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using “unadjusted” produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using “robust” produces White’s covariance estimator.
- force_int: bool =
True
¶ Whether the force the estimated optimal bandwidth to be an integer.
- df_adjust: bool =
False
¶ Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model.
- diff: bool =
- Returns:¶
The estimation results instance.
- Return type:¶