arch.unitroot.cointegration.FullyModifiedOLS.fit¶

FullyModifiedOLS.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.

Returns:¶

The estimation results instance.

Return type:¶

CointegrationAnalysisResults